Section 5 of 5
Endogeneity, IV, RDD, panel data, causal ML.
7 chapters in this section.
Endogeneity — what it is formally, why OLS becomes biased and inconsistent under it, the three main sources (omitted variables, simultaneity, measurement error) plus self-selection, how to detect it in practice, and why this is the central problem the rest of the Econometrics track is built around.
Instrumental Variables / 2SLS as the foundational tool for causal inference in observational data when the treatment is endogenous — the IV idea in plain terms, the four conditions a valid instrument must satisfy (relevance, exclusion, independence, monotonicity), the two-stage estimator, weak-instrument diagnostics beyond the F>10 rule, what IV actually identifies (LATE, not ATE), and worked instrument choices for pricing problems.
Regression discontinuity as a quasi-experimental method built on a threshold-based assignment rule — the continuity assumption that does the identification work, sharp and fuzzy variants, the modern bandwidth and diagnostic toolkit, and why the resulting effect is local to the cutoff rather than population-wide.
The control function approach as the residual-based alternative to 2SLS — when the two agree, what CF's structural assumption actually is, where an ML-flexible first stage helps and where it does not, and how the idea extends through cross-fitting into double / debiased machine learning.
Fixed effects for panel data — the within-transformation, what it controls for and what it misses, cluster-robust standard errors, the LSDV equivalence, the staggered-DiD problem, and when FE is the right tool.
Causal ML at the meeting point of econometrics and ML — ATE vs CATE, uplift modelling, DML and orthogonal-moments inference, causal forests, counterfactual prediction, off-policy evaluation, and the standard mistakes from treating predictive models as causal.
Pricing as the worked-example for the Econometrics track — why price is endogenous, the identification strategies (IV, FE, RD, DML, CATE), cross-price elasticity and cannibalization, and what-if analysis with its constant-elasticity caveats.