C

causal-inference

Reproducible causal inference and econometrics in Python: simulations, quasi-experiments, experiments, causal ML and marketing mix models, with explicit identification assumptions and diagnostics.

aGrUM is a C++ library designed for easily building applications using graphical models such as Bayesian networks, influence diagrams, decision trees, GAI networks or Markov decision processes.

Python research code that measures false-positive rates and minimum detectable lift of geo-experiment estimators by running placebo experiments on real county sales data with no intervention. Compares difference-in-differences, synthetic control, ridge-augmented SC, conformal tests and structural time series.

Python research code for synthetic control, donor-pool geometry, and placebo inference. Includes DiD, ridge-augmented SC, synthetic DiD, Monte Carlo experiments, and the California Proposition 99 case study.

Python research code comparing interrupted time series (segmented regression) and Bayesian structural time series counterfactuals. Includes a Gibbs-sampled BSTS, Monte Carlo experiments on trends, seasonality, effect shape and triggered launches, and a placebo study of the 1983 UK seat belt law.

Reproducible Python experiments turning treatment-effect estimates into targeting policies, with cross-fitted policy evaluation, uncertainty analysis, and policy trees on Hillstrom's randomised email experiment.

Profile README of Diogo Ribeiro, lead data scientist, machine learning engineer and professor working on statistical machine learning, time series, causal inference and applied AI.

Python research code for sensitivity analysis in double machine learning. A twin design, in which the hidden confounder copies an observed benchmark covariate, tests a benchmark-calibrated omitted-variable-bias bound when the benchmark is itself estimated, with an application to 401(k) eligibility and a mathematical supplement.