User Guide Foreword The need for causal inference Introduction to DoWhy Supported causal tasks Testing validity of a causal analysis Who this user guide is for Modeling Causal Relations Specifying a causal graph using domain knowledge Learning causal structure from data Graph discovery using CDT Graph discovery using dodiscover Graph discovery using causal-learn Refuting a Causal Graph Performing independence tests Graph refutations Modeling Graphical Causal Models (GCMs) Fitting an SCM to the data Evaluating a fitted SCM Related example notebooks Other topics Types of graphical causal models Generate samples from a GCM Evaluate a GCM Customizing Causal Mechanism Assignment Estimating Confidence Intervals Performing Causal Tasks Estimating Causal Effects Identifying causal effect Estimating average causal effect using backdoor Estimating average causal effect with natural experiments Estimating conditional average causal effect Estimating average causal effect using GCM Quantify Causal Influence Mediation Analysis: Estimating natural direct and indirect effects Direct Effect: Quantifying Arrow Strength Quantifying Intrinsic Causal Influence Root-Cause Analysis and Explanation Anomaly Attribution Attributing Distributional Changes Feature Relevance Asking and Answering What-If Questions Simulating the Impact of Interventions Computing Counterfactuals Predicting outcome for out-of-distribution inputs Refuting causal estimates Refuting Effect Estimates Refutations based on negative control Refutations based on sensitivity analysis Citing this package