Before we write a single line of code, let's make sure we understand what we're actually building. In plain English, the **ATE** (Average Treatment Effect) answers this question: "What would be the average difference in outcomes if everyone received the treatment versus if no one did?" It's the holy grail of causal inference.
A plain-English reference glossary covering DAGs, confounders, ATE, backdoor criterion, counterfactuals, and other causal inference terms with worked examples.
Understand how aggregated data misleads data scientists into making wrong decisions, and discover why stratifying your analysis is key to revealing the truth.
Run the complete causal workflow on a hotel case, from naive correlation through DAG, identification, estimation, and refutation to a business decision.
Learn how counterfactual reasoning with DoWhy answers what-if questions for specific individuals, using Structural Causal Models to identify who was truly savable.
Learn root cause analysis with DoWhy's GCM API: fit normal data, attribute anomalies to variables, and separate intrinsic breaks from input-driven shifts.
Learn how to move beyond average treatment effects to uncover which customer segments truly benefit from a treatment using CATE, Double Machine Learning, and uplift curves in Python.
Learn how propensity scores fix selection bias in causal inference — through matching, weighting, and the common support trap that makes both methods fail.
Discover how Instrumental Variables bypass unmeasured confounders using a random nudge and Two-Stage Least Squares to recover unbiased causal effects.
Learn how to stress-test your causal estimates against unmeasured confounders using sensitivity analysis, E-values, and tipping-point plots in DoWhy.
Learn how to use DoWhy to identify, estimate, and validate causal effects using backdoor adjustment, multiple estimation methods, and refutation tests.
Learn to specify a causal graph in DoWhy three ways, load a CausalModel with your own data, visualize assumptions, and test your DAG against the data to catch hidden bias.
Learn why correlation isn't causation, how hidden confounders distort your analysis, and preview how Python's DoWhy library recovers true causal effects from data.