Causal Inference by Paul R. Rosenbaum

Author:Paul R. Rosenbaum [Rosenbaum, Paul]
Language: eng
Format: epub
Tags: Causation; causality; clinical trials; data science; econometrics; epidemiology; health outcomes; medical statistics; randomization; observational study; policy analysis; program evaluation; propensity; public health; randomized experiment; randomized trials; sensitivity analysis; statistics; treatment effects.
Publisher: MIT Press
Published: 2023-03-02T00:00:00+00:00

Unlike a sensitivity analysis, a quasi-experimental device furnishes new data intended to advance a claim by undermining a specific counterclaim, by undermining specific grounds for doubt.

A new public policy—a change in the tax code, increase in the minimum wage, or law placing restrictions on handgun purchases—often begins abruptly on a specific date stated in an act of legislation. For people who come under the scope of the policy, before that date, everyone is a control; after that date, everyone is a treated individual. A comparison of treated individuals this year with controls from last year faces the anticipated counterclaim that this year and last year differ in many ways; for instance, the weather was awful last year and people stayed home, but the stock market crashed this year and people felt poorer. The anticipated counterclaim is that the change in policy this year was only one difference between last year and this year, and perhaps a change in outcomes from last year to this year is not an effect caused by the change in policy. What additional data might address this counterclaim?

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