Causal ML & Double Machine Learning
Studies in this cluster, in series order. Each one keeps its own URL.
AI / ML
Retrieval, embeddings, vector indexes, evals, and serving patterns for senior interviews.
Causal ML & Double Machine Learning
6 studies- 1.Causal ML & Double Machine Learning — From Association to EffectHub: correlation is not causation. Defend ATE, ATT, and CATE first, then identification, then estimators. Predictive ML is the wrong tool for treatment decisions; this cluster maps graphs, DML, CATE forests, causal time series, and health ethics.
- 2.Causal Graphs & Identification — DAGs, Confounders & BackdoorDAGs make identification inspectable before any DML fit. Distinguish confounders, colliders, and mediators; apply backdoor and positivity; name unmeasured confounding and confounding by indication.
- 3.Double Machine Learning — Nuisance Models, Orthogonalization & Cross-FittingChernozhukov DML: residual-on-residual / orthogonal scores, flexible ML nuisances, and K-fold cross-fitting so a low-dimensional ATE stays valid. DML does not invent identification — it beats naive ML-on-treatment when the backdoor set is already right.
- 4.Heterogeneous Treatment Effects — Causal Forests & Personalized MedicineCATE vs ATE: who benefits, not only whether the average helps. Causal forests and metalearners with honest splitting; personalized treatment only under overlap, multiplicity control, and validation — without overclaiming precision medicine.
- 5.Causal Time Series — ITS, Synthetic Control & Diff-in-Diff Over TimeInterrupted time series, synthetic control, and DiD over time estimate counterfactuals after a shock. Parallel trends and pre-fit beat a low forecast MAPE. Prediction under status quo is a different question — do not re-teach ARIMA, Prophet, or GBM.
- 6.Causal ML in Health — Outcomes, Bias, Ethics & ValidationHealth causal ML is a safety rail: endpoints, selection bias, confounding by indication, fairness, and validation before acting on an estimate. The bravest senior answer is often not deploying a CATE yet.