Endogeneity, Instrumental Variables, and Experimental Design

Review of Module 11 of Data Analysis for Social Scientists (MITx, edX) – Intro to Machine Learning and Data Visualisation

Endogeneity problems can occur when there is simultaneous causality (i.e. the outcome variable affects the regressor of interest). Examples include health and exercise.

Instrumental variables are a way to indirectly measure causal relationships. For example, randomly assigned scholarships can be used as an instrument for education. One challenge with using instrumental variables is that the instrument should not have a direct effect on the outcome. For example, it can be argued that scholarships create confidence which then, together with years of education, increases test scores.

When designing experiments, things to think about are: what is being randomised; who is being randomised; how is randomisation introduced; and how many units are being randomised. Randomisation could be simple, through stratification or by clustering. Experimental designs include phase-in, randomising at the cutoff, encouragement design, etc.


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