CausalLift: Python package for causality-based Uplift Modeling in real-world business
Scalable, GPU-accelerated Python library for modern difference-in-differences.
📦 R/haldensify: Highly Adaptive Lasso Conditional Density Estimation
Targeted maximum likelihood estimation (TMLE) enables the integration of machine learning approaches in comparative effectiveness studies. It is a doubly robust method, making use of both the outcome model and propensity score model to generate an unbiased estimate as long as at least one of the models is correctly specified.
Lecture slides, video recordings, and coding exercises from the 2024 Northwestern University Causal Inference Workshop. This repository is not affiliated with Northwestern University or the workshop.
GenPark AI Agent Skill - Propensity score matching and Inverse Probability Weighting (IPW) estimator calculating Average Treatment Effect (ATE) under conditional ignorability.
GenPark AI Agent Skill - Graph-theoretic backdoor criterion validator and minimal confounder adjustment set identifier ensuring unconfounded causal effect estimation.
GenPark AI Agent Skill - Pearl's 3-step counterfactual inference engine executing Abduction, Action (do-surgery), and Prediction to answer what-if causal inquiries.
GenPark AI Agent Skill - Propensity score matching and Inverse Probability Weighting (IPW) estimator calculating Average Treatment Effect (ATE) under conditional ignorability.
GenPark AI Agent Skill - Graph-theoretic backdoor criterion validator and minimal confounder adjustment set identifier ensuring unconfounded causal effect estimation.
GenPark AI Agent Skill - Instrumental Variable (IV) Two-Stage Least Squares (2SLS) causal estimator isolating unobserved confounding and testing instrument strength.
GenPark AI Agent Skill - Instrumental Variable (IV) Two-Stage Least Squares (2SLS) causal estimator isolating unobserved confounding and testing instrument strength.
GenPark AI Agent Skill - Structural Causal Model (SCM) DAG engine evaluating observational distributions and simulating Pearl's do-calculus interventions.
GenPark AI Agent Skill - Structural Causal Model (SCM) DAG engine evaluating observational distributions and simulating Pearl's do-calculus interventions.
GenPark AI Agent Skill - Pearl's 3-step counterfactual inference engine executing Abduction, Action (do-surgery), and Prediction to answer what-if causal inquiries.
do the green drivers also drive longer? --- causal identification using the propensity score approach
ultimate matching toolbox & heterogenous treatment effects in panel data
R package for the estimation of causal effects.
Learn causal inference by changing the world. Guided lessons and a sandbox with confounders, mediators, colliders, and six estimators, live in the browser
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