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 Other related GCM topics Types of graphical causal models Generate samples from 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 Estimating direct and indirect effects Explaining Observed Effects and Root-Cause Analysis Anomaly Attribution Attributing Distributional Changes Quantifying Intrinsic Causal Influence Unit Change Attribution 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