Reference: Causal Inference Glossary
A plain-English reference glossary covering DAGs, confounders, ATE, backdoor criterion, counterfactuals, and other causal inference terms with worked examples.
Causal Inference
Simpson's Paradox: When Aggregated Data Tells the Opposite Story
Understand how aggregated data misleads data scientists into making wrong decisions, and discover why stratifying your analysis is key to revealing the truth.
The End-to-End Case Study: What Should Blue Harbor Do With the 90-Day Policy?
Run the complete causal workflow on a hotel case, from naive correlation through DAG, identification, estimation, and refutation to a business decision.
Counterfactual Reasoning: What Would Have Happened?
Learn how counterfactual reasoning with DoWhy answers what-if questions for specific individuals, using Structural Causal Models to identify who was truly savable.
Root Cause Analysis: Why Did My Data Suddenly Go Weird?
Learn root cause analysis with DoWhy's GCM API: fit normal data, attribute anomalies to variables, and separate intrinsic breaks from input-driven shifts.
Beyond the Average: Finding Which Customers the Treatment Actually Helps
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.
Propensity Scores in Plain English: Matching, Weighting, and When They Fail
Learn how propensity scores fix selection bias in causal inference — through matching, weighting, and the common support trap that makes both methods fail.
Instrumental Variables: When You Can't Measure the Confounder Directly
Discover how Instrumental Variables bypass unmeasured confounders using a random nudge and Two-Stage Least Squares to recover unbiased causal effects.
Sensitivity Analysis: How Robust Is Your Estimate to an Unmeasured Confounder?
Learn how to stress-test your causal estimates against unmeasured confounders using sensitivity analysis, E-values, and tipping-point plots in DoWhy.
Estimating and Validating Causal Effects With DoWhy
Learn how to use DoWhy to identify, estimate, and validate causal effects using backdoor adjustment, multiple estimation methods, and refutation tests.
Building Your First Causal Model
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.
Correlation Isn't Causation
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.