causatr (development version)

2026-04-17 — Phase 6 chunks 6a–6b: effect modification infrastructure + IPW MSM expansion

Phase 6 chunk 6a — parse_effect_mod() infrastructure + gate refactoring.

New R/effect_modification.R with the shared effect-modification parser that all four estimation methods consult. parse_effect_mod() classifies each term in confounders as either a pure confounder or an effect-modification interaction (A:modifier), returning a structured causatr_em_info object.
Companion helpers: build_ipw_msm_formula(), build_matching_msm_formula(), and check_em_compat() provide method-specific MSM construction and validation logic.
The old blanket-reject check_confounders_no_treatment() is refactored into check_confounders_treatment(), which delegates to the new parser. Bare treatment terms (~ L + A) are still rejected; true EM terms (A:sex) now pass through the parser for method-specific dispatch.

Phase 6 chunk 6b — IPW MSM expansion for effect modification.

The IPW MSM expands from intercept-only (Y ~ 1) to Y ~ A + modifier + A:modifier when parse_effect_mod() detects an interaction term. build_ipw_msm_formula() constructs the expanded formula; compute_ipw_contrast_point() passes it to the per-intervention weighted GLM refit.
The variance engine (variance_if_ipw) generalises unchanged: J, X_star, and phi_bar all extend from p_beta = 1 to p_beta > 1.
Tests: DGP-4 truth-based simulation (binary treatment x binary modifier, sandwich SE), bootstrap parity, gcomp cross-check.

2026-04-17 — CRAN compliance: non-ASCII character cleanup

R CMD check --as-cran flags non-ASCII characters in .R files. Replaced all occurrences across 22 R/ files: em/en dashes, arrows, multiplication signs to ASCII equivalents; accented names to unaccented; Unicode math symbols to \eqn{} LaTeX in roxygen and plain ASCII in comments.

2026-04-17 — Documentation overhaul

Data-generating mechanism descriptions in vignettes. All vignettes that use simulated data now include explicit DGM descriptions before each simulation code block, providing:

A plain-language description of the causal structure.
Structural equations (SEM) in display math.
The known true value of the estimand when analytically available.

Vignettes using the NHEFS dataset now include a description of the assumed causal structure (treatment, outcome, confounders, DAG) at the start. Affected vignettes: introduction, gcomp, ipw, matching, interventions, longitudinal, triangulation, missing-data, validation.

Vignette coverage expansion. New sections in existing vignettes to cover feature combinations that had test coverage but no user-facing documentation: Gamma outcome and quasibinomial outcome (gcomp, matching), GLM+splines model specification, IPSI dose-response curve (ipw), 4-period ICE example (longitudinal), missing-data vignette (new).

Unicode-to-LaTeX conversion in docs. Converted Unicode math symbols in vignettes and CLAUDE.md to proper LaTeX for correct MathJax rendering.

Other documentation changes:

gcomp.qmd gains a callout note explaining that GAM sandwich SEs use the penalised Bayesian covariance (model$Vp) as the bread inverse.
longitudinal.qmd gains a truth reference line at E[Y] = 60 on the marginal-means comparison plot for the Table 20.1 null-effect example.
Variance-theory vignettes enable tinytable_html_mathjax for correct LaTeX rendering in table captions.
Numerical tables in variance-theory.qmd reduced from 6 to 4 significant digits for readability.

2026-04-17 — `print.causatr_result()` uses `digits = 3`

Console output for causatr_result objects now prints with digits = 3 (down from 4), matching the knit_print method for more compact display.

2026-04-16 — Phase 4 chunk 3j: count treatment IPW (Poisson / negative binomial)

causat() gains a propensity_family parameter ("poisson" or "negbin") that enables IPW estimation on integer-valued count treatments. Auto-detection never infers count (non-negative integers like age are legitimately Gaussian), so this is an explicit opt-in.

Runtime: - fit_count_density() fits either stats::glm(family = poisson()) or MASS::glm.nb() (auto-selected for "negbin" when propensity_model_fn = NULL). - evaluate_density() gains Poisson (dpois) and NB (dnbinom) branches. - make_weight_fn() gains a count closure using the log link lambda = exp(X %*% alpha). - check_intervention_family_compat() allows shift() (integer delta only) and scale_by() (inverse must preserve integer support) on count treatments; rejects static, dynamic, threshold, ipsi.

Variance: no changes needed — Poisson and NB GLMs inherit from glm, so bread_inv() and prepare_model_if() work out of the box. NB theta is treated as fixed (same convention as Gaussian sigma).

Tests: truth-based Poisson DGP (shift(1) recovers ATE = 1.5), NB parity (NB nests Poisson on the same DGP), 6 rejection snapshot tests for disallowed intervention types.

2026-04-16 — Fifth-round critical review S2: near-zero intervened density warning

compute_density_ratio_weights() now warns (class causatr_near_zero_intervened_density) when > 80% of observations have near-zero density at the intervened treatment value under shift() or scale_by(). This catches the case where the intervention pushes treatment far outside the fitted distribution’s support, producing density-ratio weights that are effectively zero for the entire sample. The Hájek estimator still runs (the math is valid — zero weights just mean no contribution), but the resulting estimate is degenerate and should not be interpreted. Previously this situation was silent.

2026-04-16 — Fifth-round critical review R1: `diagnose()` censoring alignment

diagnose() now correctly excludes censored rows when computing positivity, balance, and simple-balance diagnostics for gcomp fits that use a censoring column. Previously, compute_positivity(), compute_balance(), and compute_balance_simple() called get_fit_rows(data, outcome) without passing fit$censoring, so diagnostics were computed on the full non-NA-outcome sample (including censored individuals) while the main gcomp pipeline excluded them. The diagnostics thus described a different sample than the one used for estimation. All three sites now call get_fit_rows(data, outcome, fit$censoring). Regression test in tests/testthat/test-diagnose.R.

2026-04-15 — Fourth-round critical review: reject `na.action = na.exclude`

causat() and causat_survival() now abort via check_dots_na_action() when na.action = na.exclude is forwarded through .... Under na.exclude, stats::residuals(model, "working") is padded with NAs to the original data length while model.matrix(model) drops NA rows — the length mismatch propagated into prepare_model_if()’s r_score and then silently corrupted the Channel-2 correction via R’s recycling (only a “longer object length” warning, no abort). Sandwich SEs were mathematically wrong. Only na.omit (default) and na.fail are accepted now; users with NAs should drop them explicitly before calling causat(). New error class causatr_bad_na_action. Regression test in tests/testthat/test-causat.R.

2026-04-15 — Third-round critical review: dots audit + `replay_fit()` helper + T6/T7/T9

A full audit of the fit$details$dots plumbing (capture sites in fit_gcomp_point() / fit_ipw() / fit_matching() / fit_ice(), replay sites in refit_gcomp() / refit_ipw() / refit_matching() / ice_iterate()) flagged three systemic risks:

R1 (T2): positional dots with empty names slipped through the hand-written setdiff(names(dots), ...) strips.
R2 (C5): four duplicate-strip blocks had to stay in sync by hand.
R4: the strip lists were drifting — adding a new reserved key to one site but forgetting the others was a silent regression.

Fix — Alternative E in the audit: one central helper. New replay_fit(fn, base_args, dots, reserved) in R/utils.R owns the c(base_args, dots) + do.call(fn, ...) composition. All four refit sites (plus fit_ipw() itself) now call it. Behavior:

Unnamed (positional) entries in dots are dropped. Fixes T2.
Keys already present in base_args are stripped from dots so the caller’s explicit value always wins. Covers C5 centrally.
A reserved vector lets callers block additional keys the target function uses implicitly (e.g. WeightIt::weightit’s s.weights when external weights are not supplied).

Other non-dots fixes in the same pass:

T6 — causat_survival() now uses stats::quasibinomial() when external weights are supplied, suppressing the spurious “non-integer #successes in a binomial glm” warning without changing coefficients or SEs (same score equations, free dispersion).
T7 — bread_inv()’s singular-bread warning now carries a causatr_singular_bread class. The bootstrap withCallingHandlers in variance_bootstrap() matches on class, not on the English substring, so a future wording change cannot silently break the demotion.
T9 — causat_survival()’s internal-column strip now sources the list from a new CAUSATR_SURVIVAL_INTERNAL_COLS constant in R/utils.R (derived as setdiff(CAUSATR_RESERVED_COLS, ".pseudo_y")), so adding a new reserved column propagates automatically.

Audit items R3 (unknown dots silently ignored by target function), R5 (backward-compat with old fit objects missing $dots), R6 (replay opacity), and R7 (arg aliasing across target functions) were verified as non-bugs via direct R scripts — base R catches unknown args at fit time, %||% list() handles missing $dots, fit$details$dots is inspectable, and explicit base_args locks override dots.

Tests. New tests/testthat/test-replay-fit.R adds 14 unit tests pinning replay_fit() edge cases (base-wins precedence, positional dots dropping, reserved-key blocking, NULL dots, glm.control catching unknown args, backward-compat with missing $dots, end-to-end gcomp and IPW bootstrap replay). All 44 earlier critical-review + weight-edge-case tests still pass unchanged.

2026-04-15 — Second-round critical-review follow-ups (C3, C5, C12) + B7 gradient fix + weight edge-case tests

A second-round self-review of the 2026-04-15 critical-review sweep flagged twelve concerns (C1–C12). C1, C2, C4, C6, C7, C8, C9, C10, C11 were either false alarms (misread R semantics) or acceptable as-is. C3, C5, and C12 landed as real fixes; running the regression tests also surfaced an incomplete B7 fix. A new tests/testthat/test-weights-edge-cases.R adds 24 weight-specific tests covering every branch the critical review flagged as weight-sensitive.

C3 — Classed abort for empty target population. build_point_channel_pieces() now emits rlang::abort(class = "causatr_empty_target", ...), and the by skip path in compute_contrast() matches on class, not on the English message text. Any future wording drift can no longer silently turn skipped strata into real aborts.
C5 — Duplicate key stripping in c(args, dots) compositions. fit_ipw(), refit_ipw(), refit_gcomp(), and ice_iterate() now strip keys already present in args from the stashed dots before the c(). do.call() always takes the first named duplicate, so the previous behavior was correct but fragile — explicit stripping makes the precedence unambiguous.
C12 — .causatr_prev_event / .causatr_prev_cens stripped from fit$data. The within-id risk-set bookkeeping columns in causat_survival() are now dropped before the causatr_fit is returned, so they no longer leak into user-visible state.
B7 gradient follow-up — unify ICE step_i == 1 gradient on the weighted form. The B7 fix in the first sweep only touched the Channel-1 IF vector, not the corresponding sensitivity gradient computed at step 1 of the backward iteration, which still used n_target (now removed from the enclosing scope). Running the new regression test surfaced a n_target not found abort. The gradient is now computed via the same w_t * mu_eta / sum_w_target form and reduces to the original unweighted expression when w_t = 1.
Weight edge-case coverage. New test file exercises: NA / Inf / NaN / negative / non-numeric / wrong-length weights rejected by check_weights(); zero weights as row exclusion; uniform weights producing identical SEs to the unweighted path (gcomp + ICE); heterogeneous weights changing SEs non-trivially; IPW survey weights attaching as s.weights; matched outcome model carrying match_weight * external_weight; causat_survival() weight plumbing to the hazard fit.

2026-04-15 — Critical-review sweep (B1-B8, R3-R12)

A second full-codebase critical review flagged ~twenty correctness and robustness issues across the variance engine, bootstrap refit path, contrast() dispatch, and causat_survival(). Every blocking fix ships with a regression test in tests/testthat/test-critical-review-2026-04.R.

Blocking correctness fixes

B1 — subset expressions now resolve caller-scoped variables. contrast(fit, subset = quote(age > cutoff)) previously evaluated subset with parent.frame() deep inside internal dispatch frames, so cutoff (defined in the user’s session) failed to resolve. The caller’s frame is now captured once at contrast()’s top level and threaded through compute_contrast(), get_target_idx(), variance_bootstrap(), and ice_variance_bootstrap() as an explicit subset_env argument. get_target_idx() also validates that the evaluated expression has length nrow(data) so a scalar TRUE cannot recycle silently.
B2 — Bootstrap replicates replay the user’s ... verbatim. refit_gcomp(), refit_ipw(), and refit_matching() previously dropped the user’s extras (method = "cbps", stabilize = TRUE, caliper, ratio, gamma, GAM smoothing, etc.), so bootstrap SEs for any non-default estimator silently corresponded to a different estimator than the point estimate. fit_ipw() / fit_matching() / fit_gcomp_point() now stash list(...) onto fit$details$dots and each refit function threads those back through do.call().
B5 — causat_survival() implements the documented censoring rule. The roxygen promised “subsequent rows for that individual are also dropped”, but the implementation only excluded the current row. A .causatr_prev_cens within-id cumsum + lag column now drops all rows at and after the first censor per id.
B6 — External weights enter WeightIt as s.weights. fit_ipw() post-multiplied external weights onto w$weights after WeightIt::weightit() had finished, so the Mparts sandwich correction silently under-corrected survey-weighted IPW. External weights now flow in as s.weights, and refit_ipw() mirrors the change at bootstrap time.
B7 — ICE Channel-1 IF unified on a single formula. variance_if_ice_one() previously used (n / n_target) * (Y - mu) unweighted and n * (w / sum_w) * (Y - mu) weighted. These agree only when sum(w) == n_target, so heterogeneous weights mis-scaled the Channel-1/Channel-2 cross-term. The unweighted branch now sets w = 1 and shares the weighted formula. A regression test pins uniform-weighted and unweighted SEs to machine precision.
B8 — by skips empty strata instead of aborting the whole call. by_vals is now enumerated from fit$details$fit_rows (not the full data), and any per-level call that still ends up with an empty target is caught by a tryCatch, collected into a single rlang::warn() at the end, and dropped from the combined result.

Required robustness fixes

R3 — Branch-A propensity IF reconciles via na.action. When the MSM and propensity fits drop different rows, prepare_propensity_if_weightit() now intersects fit_idx with the MSM’s na.action instead of aborting.
R6 — OR validation no longer aborts on NA mu_hat. The mu_hat >= 1 check now pre-filters NAs consistently with the <= 0 check.
R7 — bread_inv() prefers weights(model, type = "working"). Keeps GLM subclasses (glm_weightit, glmnet’s glm wrapper) correctly aligned with the sandwich convention.
R8 — Matched cluster aggregation uses a first-seen factor. vcov_from_if(..., cluster = subclass) no longer sorts the cluster levels alphabetically / numerically, which had desynchronised rowsum(..., reorder = FALSE) from IF_mat’s row order.
R9 — causat_survival() sorts once up front via setkeyv(). Previously two by = id aggregations relied on data.table’s group-order staying consistent between calls.
R10 — Robust Mparts detection. Handles both list-shaped and flag-shaped attr(w, "Mparts") across WeightIt versions.
R11 — Dead is.integer branch removed in interventions.R. is.integer(x) already implies is.numeric(x).
R12 — Reserved column names centralised. New constant CAUSATR_RESERVED_COLS and helper check_reserved_cols() in R/utils.R, called from prepare_data(), fit_ice(), and causat_survival(). The previously-unprefixed prev_event internal column was renamed to .causatr_prev_event.

Suggestions addressed

S1 — subset length validated in get_target_idx().
S5 — Reduce(&, ...) in compute_contrast() gets an explicit init = rep(TRUE, nrow(data)) so an empty preds_list falls through cleanly.

2026-04-15 — Documentation overhaul: vignettes, math rendering, and `knit_print`

A pass over every package vignette plus a small package-level addition so that causatr_result objects render as readable HTML tables inside Quarto / knitr documents.

New package feature: knitr-aware `causatr_result` rendering

R/knit_print.R defines a knit_print.causatr_result() S3 method that renders a causat() |> contrast() result as a metadata header plus two tinytable HTML tables (intervention means + contrasts) when the result object is evaluated inside a knitr chunk. The method degrades gracefully to the existing print.causatr_result() if tinytable or knitr is unavailable.
R/zzz.R conditionally registers the method via .onLoad() (requireNamespace("knitr", quietly = TRUE)), so the dependency on knitr stays Suggests-only.
DESCRIPTION adds tinytable and litedown to Suggests (the latter is needed by tinytable::format_tt(markdown = TRUE), which the variance-theory vignette uses for math-in-cells rendering).

Vignette fixes (no behavioural changes to the engine)

longitudinal.qmd — Table 20.1 null-effect section rewritten. The previous explanation claimed “individual effects of A_0 and A_1 are both zero” and that naive regression gives non-zero estimates for both. In fact (i) the true marginal counterfactual means are all equal to 60, not the conditional effects, (ii) lm(Y ~ A0 + A1 + L1) gives A1 = 0 exactly (in the DGP Y does not depend on A1), and
1. the bias shows up in A0 only, as the conditional-within-L1 effect of $-8$. The section now shows both naive strategies (unadjusted and L1-adjusted) failing, demonstrating the null paradox as stated in Hernán & Robins Chapter 20.
by = "sex" examples fixed across three vignettes.
- gcomp.qmd now refits with an explicit qsmk:sex term in confounders, so the stratified estimates actually move (Male $\approx$ 3.61, Female $\approx$ 3.44).
- ipw.qmd and matching.qmd replace the by = "sex" code chunks with a Phase 8 callout explaining that IPW and matching wrap a saturated Y ~ A MSM and so by = "sex" is a no-op until the unified effect-modification API lands (PHASE_6_INTERACTIONS.md).
Unified “Summary of covered combinations” tables. gcomp, ipw, matching, and longitudinal now share one 8-column schema (Treatment | Outcome | Intervention | Estimand | Contrast | Inference | Weights | Status) with a common ✅ / 🟡 / ⛔ legend and a pointer to FEATURE_COVERAGE_MATRIX.md.
code-fold: show + code-tools: true in every vignette YAML. Combined with the new knit_print hook, readers can fold the R code of any example chunk and still see a nicely formatted result table.

`variance-theory.qmd` — roadmap + verified numerics

New Section 0 “Roadmap” walks through Sections 1 $\to$ 6 as a single narrative and introduces the running example (a small logistic GLM) used by every verification block below.
Eight new Math / Code / Result tabset panels, each of which instantiates a key equation on the running example and checks it against either sandwich::sandwich(), sandwich::vcovCL(), or causatr::variance_if():
1. Sec 1.4 — Sandwich variance of $\hat{\beta}$ vs sandwich::sandwich(fit) (agreement ≲ 1e-8).
2. Sec 2.3 — $(1/n^2) \sum IF_i IF_i^\top$ equals the sandwich to machine precision.
3. Sec 2.4 — IF for a sample mean reproduces the textbook $s/\sqrt n$ up to the $(n-1)/n$ d.f. correction.
4. Sec 3.2 — Two-channel IF for g-comp $\hat\mu_1$ matches causatr’s sandwich SE to $$1e-9.
5. Sec 3.3 — Delta-method decomposition: shows numerically that $J V_\beta J^\top$ equals $\text{Ch.2}^2$, and that dropping $\text{Ch.1}^2$ + cross term underestimates the SE by about 3% on this example.
6. Sec 4.2 — Horvitz-Thompson IPW $\hat\mu_1$ equals causatr’s IPW point estimate, with SE from the propensity-corrected adjusted score.
7. Sec 4.3 — Cluster-robust sandwich: per-cluster IFs summed before squaring reproduce sandwich::vcovCL(..., cadjust = FALSE) on a toy subclass assignment.
8. Sec 5.5 — ICE chained IF on a 2-period linear-Gaussian DGP recovers the structural truth $0.8 + 0.6 = 1.4$ within the sandwich CI.

`validation.qmd` — tinytable comparisons

compare_row() now returns a tinytable with a third row of absolute differences between causatr and each reference implementation (stdReg2, WeightIt, marginaleffects, lmtp). Agreement — or the lack of it — is readable without having to compare 6 decimal places by eye.

Breaking: `causat()` / `causat_survival()` argument `method` renamed to `estimator`

causat(method = ...) shadowed WeightIt::weightit(method = ...) and MatchIt::matchit(method = ...), making it impossible to forward those packages’ own method selector through .... The causatr argument has been renamed to estimator; extra ... arguments now flow cleanly into WeightIt / MatchIt. This is a hard rename with no deprecation shim — update call sites from method = "gcomp" | "ipw" | "matching" to estimator = "...", and from fit$method / result$method / diag$method to fit$estimator / result$estimator / diag$estimator. The glance.causatr_result() output column is renamed in the same way.

After the rename, forwarding e.g. a CBPS propensity model is a one-liner:

fit <- causat(data, outcome = "Y", treatment = "A", confounders = ~ L1 + L2,
              estimator = "ipw", method = "cbps")

See vignette("ipw") for a worked example. ci_method is a separate concept and is unchanged.

2026-04-14 — Critical review sweep (R1–R9 + S1–S9)

Systematic audit and hardening of the whole package in a single pass. All fixes below are covered by existing or newly-added tests; the FEATURE_COVERAGE_MATRIX.md now carries a CI-enforced round-trip check so future drift between the matrix and the test files fails loudly.

Required changes (correctness / silent-failure fixes)

contrast() ratio / odds-ratio guard (R2). The log-scale delta method was silently returning NaN CIs when mu_hat had mixed signs (legal for Gaussian outcomes, fatal for log(R)). contrast(type = "ratio") now aborts upfront if any intervention mean is non-positive, and type = "or" additionally aborts if any mean is $\geq 1$. Error messages point to type = "difference" or a binomial / poisson / gamma refit.
variance_bootstrap() warning suppression narrowed (R3). The refit closure previously wrapped the whole pipeline in suppressWarnings(), hiding GLM non-convergence, bread-singular warnings, and everything else. Replaced with a withCallingHandlers() that demotes only the three expected noisy warnings (fitted probabilities numerically 0 or 1, X'WX is singular, Fewer (control|treated) units). Genuine failures now surface through tryCatch’s error path and show up as failed replicates in boot_info.
Shared match-weight alignment helper (R4). Extracted combine_match_and_external_weights() (in R/matching.R) so both fit_matching() and refit_matching() share the rowname-invariant check. A future change to as.data.frame.data.table’s rowname behavior now fails loudly in exactly one place instead of silently producing misaligned weights in the bootstrap.
dynamic() docstring corrected (R5). The previous wording claimed the rule received data “subset to the current time point”; in reality it is called once per intervention with the full counterfactual panel. Updated the roxygen prose and the example to show how to branch on the time column or a lag column.
ICE all-NA column drop is no longer silent (R6). ice_build_formula() still drops lag*_ columns at step 0 (a structural requirement), but now emits a rlang::inform() when a user-supplied column is dropped at a step because it happened to be all NA (e.g. a time-varying confounder first measured at time > 0). Gated via .frequency = "regularly" so the message does not spam long pipelines.
boot_info$n_fail_by_int (R7). process_boot_results() now records per-intervention NA counts in addition to the whole-row totals. This lets users see whether bootstrap failures cluster on a single intervention (e.g. a rare static() level triggering separation) versus being spread across the whole replicate. The field is surfaced in boot_info and tested in test-s3-methods.R.
causat_survival() wide-format check (R8). The previous max(rows_per_id$N) == 1L test only caught uniformly wide data; a mixed frame with some single-row ids and some multi-row ids would slip through. Now uses any(rows_per_id$N == 1L) and additionally asserts that time varies within each id (no duplicated rows at the same time value).
FEATURE_COVERAGE_MATRIX.md ↔︎ tests/ round-trip check (R9). New tests/testthat/test-coverage-matrix.R parses the matrix, extracts every test-*.R reference, and asserts both that the referenced file exists and that every test file on disk is referenced somewhere in the matrix. Guards against the exact kind of drift the rest of this sweep was cleaning up.

Suggestions (hardening / clarity)

fit_gcomp_point() resolves family via resolve_family() (S1) so custom model_fn implementations that don’t auto-coerce a bare string see a fully-evaluated family object. Matches the behavior already in fit_ipw() and fit_matching().
Multivariate sub-intervention name validation (S2). apply_intervention() now asserts setequal(names(iv), treatment) before iterating the sub-interventions. A typo like list(X = static(1), A2 = static(0)) for treatment = c("A1", "A2") now aborts with a clear message instead of silently creating a spurious X column via data.table.
ipsi() constructor informs about its Phase-4 status (S3). The constructor succeeds (so users can build their interventions list) but surfaces a one-per-session rlang::inform() pointing to the known contrast() abort, so users hit the wall at construction rather than deep in a pipeline. Use .frequency_id = "causatr_ipsi_dead_end" to silence or grep for it.
check_estimand_trt_compat() error message (S4) now explicitly says “binary point treatments coded as 0/1” and suggests recoding. Factor- or 1/2-coded treatments previously got the same message without any hint at the coding issue.
Tier 2 numeric variance warning is now classed (S5). variance_if_numeric()’s delta-shortcut warning carries class = "causatr_tier2_fallback" so batch pipelines and CI can grep for it via withCallingHandlers().
compute_weight_summary() binary split pulls levels from WeightIt (S6) instead of hard-coding trt == 0 / trt == 1. Factor-coded binary treatments and integer codings like c(1, 2) now get correctly-populated “treated” / “control” rows.
contrast() subset evaluation preserves lexical scope (S7). The three eval(subset, envir = as.list(data)) call sites were copying every column of data into a fresh list and losing the caller’s enclosing environment. Switched to eval(subset, envir = data, enclos = parent.frame()), which skips the copy and lets subset expressions reference session variables like quote(age > cutoff).
FEATURE_COVERAGE_MATRIX survival section split (S8). Replaced the mixed “fit-only smoke (Phase 6 contrast pending)” rows with an explicit three-table layout (fit path / contrast path / rejection path) so the Phase 6 gap is visible without status annotations that mean different things on different rows.
.pseudo_y column collision guard in ICE (S9). fit_ice() now aborts upfront if data already has a column named .pseudo_y, which is the internal bookkeeping column ICE writes predicted pseudo-outcomes into during the backward iteration.

Doc drift — `A:modifier` behaviour (R1)

CLAUDE.md, NEWS.md (this file’s older entries), PHASE_6_INTERACTIONS.md, and CAUSATR_SCAFFOLD.md all still described IPW and matching as “silently ignoring” A:modifier interaction terms in confounders. That code path was hardened months ago: fit_ipw() and fit_matching() now call check_confounders_no_treatment() and abort upfront with a Phase-8 pointer. Every doc that mis-described the old behavior has been updated to match the current behavior.

Vignette expansion

Every method vignette (excluding variance-theory.qmd) now covers every combination supported by FEATURE_COVERAGE_MATRIX.md:

gcomp.qmd — added worked examples for categorical (k > 2) treatment, multivariate (joint) treatment, external survey weights, and a Poisson count outcome with a log-link ratio contrast. Summary table now records the family / weights columns.
ipw.qmd — added categorical and continuous (GPS) treatment sections, an external-survey-weights section, a parallel-bootstrap section, and a non-Mparts WeightIt fallback example. Updated the Channel-2 technical note to reference the current prepare_propensity_if() / apply_propensity_correction() architecture (was still referring to the renamed correct_propensity()).
matching.qmd — added an ATT + external survey weights section demonstrating combine_match_and_external_weights(), and extended the summary table with a categorical / continuous rejection row.
longitudinal.qmd — added continuous-treatment + MTP section (shift / scale_by / threshold), multivariate time-varying treatment, external weights (with a note pointing to the ICE cascade-gradient correction in §5.4 of the theory vignette), and a censoring / IPCW-style example.
survival.qmd — replaced the 3-line TODO stub with a full narrative of the current fit-only Phase 6 status, the intended end-to-end contrast algorithm, and the tracking pointers.
triangulation.qmd — introduction.qmd and the comparison table both corrected for the drift above (matching is binary-only; continuous / categorical are not “Limited”).

2026-04-14 — Sweep and audit

ICE sandwich fix under non-uniform external weights. The cascade gradient at step k > 0 was dropping the w_{k-1} factor from $A_{k-1,k} = E[\partial s_{k-1} / \partial \beta_k]$, silently underestimating the SE by ~2x on multi-period DGPs. Verified against lmtp::lmtp_tmle and a 300-replicate Monte-Carlo. (Followed by a separate fix for the earlier regression where the ICE backward loop dropped external weights entirely.)
ICE double-counting fix for shift/scale_by/threshold. ice_apply_intervention_long() was recomputing lag columns from the intervened treatment, double-counting the shift through both the lag path and the current-A prediction path. Now matches lmtp on the canonical linear-Gaussian DGP.
New FEATURE_COVERAGE_MATRIX.md. Single source of truth for what’s tested and at what fidelity (truth / smoke / none / rejected). New test-writing rule in CLAUDE.md: every feature change MUST update the matrix in the same PR.
Coverage sweep closing most residual gaps:
- gcomp × quasibinomial, Gamma(log), probit, cloglog, Poisson × bootstrap, categorical (3-level) treatment
- IPW × categorical (3-level), continuous, survey weights, ATT bootstrap, non-Mparts WeightIt method at contrast time
- matching × Poisson, Gamma(log), ATC bootstrap, categorical rejection (MatchIt is binary-only; verified against upstream docs)
- ICE × scale_by, threshold, shift bootstrap, bootstrap × survey weights, binomial × ratio/OR, by(sex) stratification, external-weights truth test
- Survival × censored fit (smoke; contrasts still Phase 6)
- End-to-end Tier 2 numeric variance via a custom model_fn
- vcov_from_if(cluster = ...) unit test vs hand-computed reference
- confint() level monotonicity on both sandwich and bootstrap
Defensive guards across causat(), causat_survival(), IPW, matching, ICE, diagnose. Front-door validation for NA / Inf / negative / mis-sized / non-numeric weights; rejection of duplicated or empty intervention names; explicit longitudinal rejection in diagnose() (previously crashed inside cobalt); categorical treatment rejection in matching with a clearer causatr-side message.
~ sex + A:sex no longer triggers a false-positive “Baseline confounder ‘A’ varies within some individuals” warning. Treatment columns are now excluded from warn_confounder_variation().
altdoc sidebar fix. Single-item sections (“Getting started”, “Theory”) were silently dropped from the deployed site because yaml::yaml.load() collapses a one-element scalar sequence into a scalar that Quarto’s sidebar can’t parse. Both sections now use the explicit text:/file: form, making the variance-theory vignette visible on the website.
vignettes/variance-theory.qmd Section 5.4 now derives the ICE cascade gradient with the explicit w_{k-1} factor and the pseudo-code shows the prior.weights lookup.
PHASE_6_INTERACTIONS.md design doc. Plans a unified effect-modification API across gcomp / IPW / matching / ICE. IPW and matching currently hardcode a saturated Y ~ A MSM and hard-abort on any A:modifier term in confounders via check_confounders_no_treatment() (rather than silently dropping it and returning a pooled ATE); ICE handles current-period A × modifier but not lagged A × modifier. Phase 8 will replace both the abort and the lag-gap with a proper MSM builder.

2026-04-13 — Variance engine unification

Single influence-function engine. Replaced four method-specific variance paths with one variance_if() dispatcher that routes to variance_if_gcomp() / variance_if_ipw() / variance_if_matching() / variance_if_ice(). All four share prepare_model_if() / apply_model_correction() primitives and vcov_from_if() aggregator.
Prep / apply split. prepare_model_if() computes the gradient-independent half of Channel 2 once per model; apply_model_correction() finishes it per intervention. Saves an O(k) bread solve when contrast() is called with many interventions.
Matching uses cluster = subclass in the aggregator (cluster-robust IF-level sum-then-square).

2026-04-12 — Large refactor + feature pass

Point-treatment sandwich now uses the full influence function (previous version was effectively a delta-method shortcut and could underestimate the SE on non-saturated MSMs).
variance_if() as single entry point. Introduces correct_model() and correct_propensity() as the two per-model correction kernels. New vignettes/variance-theory.qmd derives the two-channel IF from first principles.
Log-scale CIs for ratio and or contrasts. Respects the (0, ∞) support and gives better coverage than Wald bounds.
static() accepts character values for categorical treatments (e.g. static("never")).
Analytical Jacobian for GLMs in sandwich variance (previously used numerical differencing).
External weights moved to fit$details$weights (never a column in data) so every method consumes them uniformly.
New test-complex-dgp.R covering nonlinear confounding, GAMs, splines, and multi-confounder designs.
ICE IF scaling fix for subset / by-stratified targets, plus a confint() ordering fix and a vectorized IF loop for speed.

2026-04-11 — Bootstrap percentile CIs

Bootstrap path stores raw replicates in object$boot_t and confint() recomputes percentile CIs from them per level. The sandwich path continues to reconstruct Wald CIs from the stored SE.
External weights now propagate through every method (gcomp, IPW, matching, ICE); previously ICE dropped them in the backward loop.

2026-04-10 — Polish and hardening

by-stratification vcov fix — stratum-specific vcov matrices now land in res$vcov[[level]] as a list instead of being collapsed to the pooled matrix.
family now threads through IPW and matching (previously silently forced Gaussian on the MSM regardless of the user’s family argument).
scale() $\to$ scale_by() rename to avoid colliding with the base R scale().
by + estimand bug fix — previously the ATT/ATC restriction and the by-level restriction fought over the target population and returned the wrong per-stratum estimate.
type parameter on causat() for explicit point / longitudinal selection; autodetects from id + time otherwise.

2026-04-09 — Effect modification, S3 polish, broom integration

New by argument on contrast() for stratified (by-level) effect estimates. Computes separate target means and vcov per stratum; confint() and tidy() respect the stratification.
broom integration via generics — tidy.causatr_result() and glance.causatr_result() return the expected columns.
Forest plots via the forrest package for causatr_result objects.
S3 method polish across print, summary, plot, coef, vcov, confint, tidy, glance.

2026-04-02 / 2026-04-03 — Longitudinal scaffolding + R CMD check

ICE sandwich variance with scaling fix; first batch of simulation-based ICE tests.
Clean R CMD check: man pages regenerated, MASS / knitr declared, Windows vignette skip to work around a toolchain issue.

2026-04-01 — Phase 2–3 implementation

causat(estimator = "gcomp" / "ipw" / "matching") fully working, with:
- parametric g-formula via a user-supplied model_fn
- IPW via WeightIt::weightit() and WeightIt::glm_weightit()
- matching via MatchIt::matchit() + weighted MSM
diagnose() for all three methods: positivity, balance (via cobalt), weight summaries, match quality, Love plots.
First vignettes: gcomp.qmd, ipw.qmd, matching.qmd, triangulation.qmd.

2026-03-30 / 2026-03-31 — Phase 1 scaffolding

Initial package structure: DESCRIPTION, NAMESPACE, R/ layout, testthat setup, dev tooling, GitHub Actions CI.
Core API stubs: causat(), contrast(), diagnose(), intervention constructors (static, shift, scale_by, threshold, dynamic, ipsi), S3 classes.
NHEFS dataset bundled; input-validation helpers in checks.R.