TMLE (Targeted Maximum Likelihood Estimation):
Added method = "tmle" for doubly-robust estimation using
the tmle package with SuperLearner.
MatchIt Integration: Proper propensity score
matching via method = "matching" using the
MatchIt package with nearest neighbor matching.
Causal Forests (grf): Added
method = "grf" for heterogeneous treatment effect
estimation using Generalized Random Forests.
Cox IPTW for Survival: Added
method = "cox_iptw" for survival outcomes, implementing
stabilized inverse probability weighted Cox models on compatible
survival runtimes.
Front-Door Kernel
(frontdoor_effect()): Implements the front-door kernel
existence result (thm:frontdoor) with a plugin estimator
and a heuristic front-door deficiency proxy.
Transport Deficiency
(transport_deficiency()): Measures distribution shift
between source and target populations with proxy diagnostics.
Instrumental Variables
(iv_effect()): IV support with 2SLS and Wald estimators,
plus weak instrument diagnostics and validity tests via
test_instrument().
causal_spec_competing()): Full support for time-to-event
data with multiple event types. Implements cause-specific and
subdistribution hazard estimation via
estimate_deficiency_competing().Parallel Bootstrap: New
parallel = TRUE argument in
estimate_deficiency() enables parallel processing via
future.apply for faster inference with large bootstrap
samples.
Stabilized IPTW Weights: Propensity scores are now bounded to [0.01, 0.99] to prevent extreme weights.
Shiny Dashboard
(run_causaldef_app()): Interactive web application for
deficiency analysis with data upload, method comparison, and report
export.
Standalone Deployment
(create_shiny_app_files()): Generate app files for
shinyapps.io or Shiny Server deployment.
REST API (create_plumber_api(),
run_causaldef_api()): Full REST API via plumber for SaaS
deployment. Includes endpoints for deficiency estimation, policy bounds,
confounding frontiers, and transport analysis. Docker-ready.
negative_controls.Rmd: Comprehensive guide to using
negative control diagnostics with the negative control bound
(thm:nc_bound).
policy_learning.Rmd: Guide to safe policy learning
with decision-theoretic bounds and the safety floor concept.
inst/examples/complete_demo.Rtmle,
MatchIt, grf, SuperLearner,
future.apply, shiny, cmprsk,
plumber, jsonlitematching methodfrontdoor(method = "dr") and
iv_effect(method = "liml")nc_diagnostic() into permutation-based
screening plus kappa-sensitivity boundssurvival internals require base::deparse1policy_regret_bound() method selection explicit
and recorded optimistic post-selection when usedcausal_spec(),
causal_spec_survival(), estimate_deficiency(),
nc_diagnostic(), confounding_frontier(),
policy_regret_bound()unadjusted, iptw,
aipw