Mostly Harmless Econometrics¶
Angrist, J. D., & Pischke, J. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press.
Cited by¶
9 citations across 9 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.
Supported in partVerified against the work's full text
Supplies the frame that natural experiments approximate a real experiment from observational data; the text held does not reach difference-in-differences or regression discontinuity.
“Angrist and Krueger (1991) used the interaction between compulsory attendance laws in American states and students’ season of birth as a natural experiment”
- 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.
Supported in partVerified against the work's full text
The introduction supports the general idea that natural experiments approximate unavailable ideal experiments for causal identification, but not twins, regression discontinuity, geographic boundaries or the minimal-pair framing.
“season of birth as a natural experiment to estimate the causal effects of”
- 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.
SupportedVerified against the source
- 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
- Difference-in-Differences
- When parallel trends holds, the DiD estimator is an unbiased estimate of the average treatment effect on the treated
This sourceParallel trends as the identifying assumption under which the double difference recovers the average treatment effect on the treated.
- When parallel trends holds, the DiD estimator is an unbiased estimate of the average treatment effect on the treated
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.
- Control Group Comparison
- So the chain compares the 200 retrofit stores against a matched set of non-retrofit stores over the same window — a difference-in-differences read.
This sourceDefines difference-in-differences as a treated-versus-comparison change contrast over the same time window.
- So the chain compares the 200 retrofit stores against a matched set of non-retrofit stores over the same window — a difference-in-differences read.
- 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¶
Does it exist? Confirmed. This work's DOI resolves to a registered record, which fixes its identity. That is all it fixes.
Does it back the claim? Read against the text for 3 of 9 citations: 1 supported, 2 supported in part. Each verdict is shown under its citation below, with what in the work backs the sentence.
Support is checked per citation rather than per work — the same source can be cited soundly in one article and wrongly in another. Per-citation recording began recently, so a citation with no recorded check is a gap in the record rather than evidence it went unchecked.
See how references were verified.
Links previously used in the corpus¶
Before the registry existed this work was also linked 3 other ways.
- https://doi.org/10.2307/j.ctvcm4j72 ×3
- https://press.princeton.edu/books/paperback/9780691120355/mostly-harmless-econometrics ×2
- https://assets.press.princeton.edu/chapters/s8769.pdf ×1
Registry ID ref:3ea33f3c00c7 · see in the full table