Changes in version 0.2.0 (2026-03-31)                  

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

                        Changes in version 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

                        Changes in version 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"