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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”

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.