A comprehensive guide to causal inference—from fundamental concepts to advanced econometric methods and causal machine learning.
What you'll learn: How to move beyond correlation to answer causal questions—does X cause Y? What would have happened if we had done Z instead? This series covers the essential frameworks, methods, and practical applications for rigorous causal reasoning in business and research.
Rubin Causal Model, ATE, CATE, and the fundamental problem of causal inference. Why correlation ≠ causation.
Directed Acyclic Graphs, d-separation, backdoor/frontdoor criteria, and identifying confounders vs colliders.
Exact matching, propensity score estimation, matching/weighting/stratification, and assessing overlap.
Regression adjustment, difference-in-differences, IV/2SLS, regression discontinuity, and fixed effects.
Neyman orthogonality, cross-fitting, DML for treatment effects, and handling high-dimensional confounders.
Causal trees, honest forests, generalized random forests, and variable importance for heterogeneous effects.
S-learner, T-learner, X-learner, DR-learner, R-learner—comparison and practical uplift modeling.
Treatment-agnostic representations, balancing representations (CFR, TARNET), adversarial learning, CEVAE.
DeepIV for instrumental variables, neural tangent kernels, structure learning, and DAG optimization.
Synthetic control methods, time-varying treatments, marginal structural models, and sequential treatments.
Off-policy evaluation, contextual bandits, counterfactual reasoning in RL, and safe policy learning.
A/B testing platforms, causal ML in tech (ads, recommendations), healthcare, and policy evaluation.
Model validation, sensitivity analysis, handling assumption violations, ethics, and production systems.
This series is designed to be read sequentially, with each article building on concepts from previous ones. However, if you're already familiar with the basics, feel free to jump to specific topics of interest.