causaldef 0.2.0

Major Changes

New Estimation Methods

• 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.

New Identification Methods

• 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().

New Outcome Types

• Competing Risks (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().

Performance Improvements

• 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.

Commercial Features

• 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.

New Vignettes

• 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.

Infrastructure

• Added comprehensive demo script: inst/examples/complete_demo.R
• Added pkgdown configuration for documentation website
• Added new test suites for all new functions
• Expanded DESCRIPTION suggests to include: tmle, MatchIt, grf, SuperLearner, future.apply, shiny, cmprsk, plumber, jsonlite

Bug Fixes

• Fixed weight normalization in matching method
• Improved fallback handling when optional packages fail
• Clarified package semantics so theorem-backed bounds, computable proxies, sensitivity diagnostics, and heuristic modules are labeled distinctly
• Disabled unsupported frontdoor(method = "dr") and iv_effect(method = "liml")
• Reworked nc_diagnostic() into permutation-based screening plus kappa-sensitivity bounds
• Added explicit survival runtime guards for older R versions where survival internals require base::deparse1
• Made policy_regret_bound() method selection explicit and recorded optimistic post-selection when used

causaldef 0.1.3

• Initial CRAN submission version
• Core functions: causal_spec(), causal_spec_survival(), estimate_deficiency(), nc_diagnostic(), confounding_frontier(), policy_regret_bound()
• Basic methods: unadjusted, iptw, aipw
• Four vignettes covering methodology and survival analysis

causaldef 0.1.0

• Initial development version
• Implements Le Cam deficiency theory for causal inference
• Based on Akdemir (2026) “Constraints on Causal Inference as Experiment Comparison”