[Review] Mostly Harmless Econometrics: An Empiricist's Companion (Joshua D. Angrist) Summarized

Mostly Harmless Econometrics: An Empiricist's Companion (Joshua D. Angrist)

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#appliedeconometrics #causalinference #instrumentalvariables #differenceindifferences #regressiondiscontinuity #MostlyHarmlessEconometrics

These are takeaways from this book.

Firstly, The Credibility Mindset and the Logic of Identification, A central theme is that good econometrics starts with a research design, not a clever regression. The book pushes readers to ask what variation identifies the causal effect and whether that variation is plausibly as good as random. This mindset frames empirical work as a sequence of choices: define the causal question, specify the treatment and outcome, and then defend the comparison group. Instead of relying on broad functional form assumptions, it emphasizes threats to validity such as omitted variables, selection into treatment, reverse causality, and measurement error. Readers learn to think in terms of counterfactuals, and to judge whether an estimating strategy recovers the average causal effect for the population of interest or only for a specific subgroup. The discussion also highlights why standard regression output can be misleading if identification is weak or if standard errors fail to reflect the true uncertainty. Practical guidance includes how to interpret coefficients causally, how to use control variables responsibly, and how to diagnose when adding controls changes the estimand. The overall message is that transparent assumptions and defensible sources of variation are the foundation of trustworthy applied results.

Secondly, Regression as a Tool for Causal Comparisons, The book treats regression as a flexible way to implement causal comparisons when the identifying variation is credible. It explains how regression connects to simple differences in means, how controls can adjust for observable confounding, and why the meaning of a coefficient depends on the underlying design. A major focus is on interpreting regression as conditional comparisons: the estimated effect compares treated and untreated units that are similar on included covariates. The authors stress that including many controls is not automatically a cure for bias, because unobservables can still confound the relationship and because over controlling can remove meaningful variation. Readers are guided through typical empirical challenges such as multicollinearity, functional form choices, and the use of nonlinear models, with an emphasis on when these choices matter for causal inference. The text also underscores the importance of correct standard errors, especially under heteroskedasticity and when data are clustered in groups like schools, firms, or regions. By tying regression mechanics to identification, the book helps readers move from running regressions to explaining what their regression is actually comparing, and why that comparison should be interpreted as causal.

Thirdly, Instrumental Variables and Local Causal Effects, Instrumental variables are presented as a workhorse method for dealing with endogeneity when a valid source of exogenous variation exists. The book explains the two key requirements for an instrument: it must shift the treatment and it must affect the outcome only through that treatment. It also clarifies why these conditions are strong and why instruments must be defended using institutional knowledge and careful argument, not just statistical tests. A major contribution of the modern IV perspective is the interpretation of IV estimates as local average treatment effects under common assumptions, meaning the estimate applies to compliers whose treatment status is changed by the instrument. This local nature has important implications for external validity and policy interpretation. The authors discuss weak instruments and why they can produce misleading estimates and confidence intervals, motivating diagnostics and robust inference. They also connect IV to two stage least squares, showing how it can be understood as a particular weighting of causal effects. By combining intuition with practical warnings, the book teaches readers how to use IV responsibly, how to interpret what IV identifies, and how to communicate results without overstating what the instrument can deliver.

Fourthly, Difference in Differences and Panel Data Strategies, Difference in differences is introdu