Section 8 of 9
Endogeneity, IV, RDD, panel data, causal ML.
15 items 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.
Run a regression on anything and it hands back a number. That number is the causal effect only if the regressor and the error term don't move together. Endogeneity is the name for that assumption breaking — and why more data can't fix it.
Two words that sound like heavy jargon but name one yes/no question: does your regressor move with the error term? Clean is exogenous, contaminated is endogenous — and 'strict' just extends the question across time.
If your regressor is contaminated, borrow a clean source of variation that pushes it around. That borrowed lever is an instrument, and two-stage least squares is the machine that uses it.
Watch the same units over time and you get a free superpower: you can subtract away everything about a unit that never changes — including the confounders you can't even measure.
A cutoff nobody chose for your benefit — a passing score, an income line, an age limit — is a natural experiment hiding in plain sight. Just above and just below the line, units are near-identical except for the treatment.
Two-stage least squares replaces the dirty regressor with a clean copy. The control function does the opposite — it keeps the dirty regressor and adds one column that soaks up the dirt. In the linear case they land on the exact same number.
Elasticity is the percent change in quantity per percent change in price. It looks like one tidy regression of quantity on price — and that regression is wrong in the exact direction that costs you money.
The treatment lifts conversion 3%. For whom? An average is one number, and a single number can't tell 'helps everyone a little' apart from 'helps half a lot and quietly harms the rest.'