Mostly Harmless Econometrics¶
Angrist, J. D., & Pischke, J. (2009). Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton University Press.
Cited by¶
7 citations across 7 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Confounding
- These schools differ on leadership quality, staff engagement, and organizational culture — all confounders with student outcomes. (b) Student population differences: The 11 SLC schools had slightly lower incoming 8-grade assessment scores but higher attendance rates and lower mobility rates, suggesting systematically different student populations. © District resource allocation: SLC schools received additional implementation support, professional development, and counseling resources during the transition — the "SLC effect" on outcomes bundled together the structural reform with the resource infusion
This sourceCredibility-revolution text on design-based identification (IV, RD, difference-in-differences, natural experiments) used to sidestep unmeasured confounding; general-methods support for D25-026 and D25-030.
- These schools differ on leadership quality, staff engagement, and organizational culture — all confounders with student outcomes. (b) Student population differences: The 11 SLC schools had slightly lower incoming 8-grade assessment scores but higher attendance rates and lower mobility rates, suggesting systematically different student populations. © District resource allocation: SLC schools received additional implementation support, professional development, and counseling resources during the transition — the "SLC effect" on outcomes bundled together the structural reform with the resource infusion
- Intervention
- In public policy and economics, regulatory changes and program rollouts are interventions, and natural experiments, regression discontinuities, and difference-in-differences attempt to recover the structural break from observational data when experimentation is impossible.
This sourceStandard reference for recovering intervention-quality causal inference from observational data via difference-in-differences, instrumental variables, and regression discontinuity.
- In public policy and economics, regulatory changes and program rollouts are interventions, and natural experiments, regression discontinuities, and difference-in-differences attempt to recover the structural break from observational data when experimentation is impossible.
- Minimal Pairs
- Knockout animals are minimal pairs at the genetic level. Debugging via bisection. Locate the smallest commit-pair bracketing a regression; the bisection search hunts for the relevant minimal pair. Natural experiments. Twins reared apart, regression-discontinuity at a cutoff, geographic boundaries — the world's accidental minimal pairs, used to identify causal effects where deliberate construction is impossible.
This sourceStandard treatment of natural experiments — instrumental variables, regression discontinuity, differences-in-differences — as the world's accidental controlled contrasts for causal identification.
- Knockout animals are minimal pairs at the genetic level. Debugging via bisection. Locate the smallest commit-pair bracketing a regression; the bisection search hunts for the relevant minimal pair. Natural experiments. Twins reared apart, regression-discontinuity at a cutoff, geographic boundaries — the world's accidental minimal pairs, used to identify causal effects where deliberate construction is impossible.
- Regression to the Mean
Domain-specific¶
- Causal Inference
- Conditional exchangeability licenses adjustment for measured confounders; a valid instrument isolates exogenous treatment variation; continuity supports comparison around a cutoff; parallel trends supports a difference-in-differences contrast
This sourceTreats all four identification strategies in one text: regression under the conditional independence assumption, instrumental variables, differences-in-differences, and regression discontinuity.
- Conditional exchangeability licenses adjustment for measured confounders; a valid instrument isolates exogenous treatment variation; continuity supports comparison around a cutoff; parallel trends supports a difference-in-differences contrast
Mechanisms¶
- Causal Identification Probe
- Neighboring towns with similar labor markets but no program become a comparison group; the question becomes not "did participants improve?" but "did participants improve relative to otherwise-similar non-participants over the same window?" — a difference-in-differences contrast.
This sourceIdentifies the comparison as a difference-in-differences contrast.
- Neighboring towns with similar labor markets but no program become a comparison group; the question becomes not "did participants improve?" but "did participants improve relative to otherwise-similar non-participants over the same window?" — a difference-in-differences contrast.
- Controlled Before–After Contrast
- This difference-in-differences logic is the workhorse of applied program evaluation precisely because subtracting a comparison group's trend removes what both groups would have done anyway.
This sourceFormalizes difference-in-differences by subtracting the comparison group's common time change from the treated group's change.
- This difference-in-differences logic is the workhorse of applied program evaluation precisely because subtracting a comparison group's trend removes what both groups would have done anyway.
Verification¶
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Links previously used in the corpus¶
Before the registry existed this work was also linked 3 other ways.
- https://press.princeton.edu/books/paperback/9780691120355/mostly-harmless-econometrics ×2
- https://assets.press.princeton.edu/chapters/s8769.pdf ×1
- https://doi.org/10.2307/j.ctvcm4j72 ×1
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