Causal Inference

Reflections on Heckman and Pinto’s “Causal Analysis After Haavelmo”

Novemver, 2013


This paper reflects on a recent article by Heckman and Pinto (2013) in which they discuss a formal system, called do-calculus, that operationalizes Haavelmo’s conception of policy intervention. They replace the do-operator with an equivalent operator called “fix,” highlight the capabilities of “fix,” discover limitations in “do,” and inform readers that those limitations disappear in “the Haavelmo approach.” I examine the logic of HP’s paper, its factual basis, and its impact on econometric research and education.

Linear Models: A Useful “Microscope” for Causal Analysis

May, 2013


This note reviews basic techniques of linear path analysis and demonstrates, using simple examples, how causal phenomena of non-trivial character can be understood, exemplified and analyzed using diagrams and a few algebraic steps. The techniques allow for swift assessment of how various features of the model impact the phenomenon under investigation. This includes: Simpson’s paradox, case–control bias, selection bias, missing data, collider bias, reverse regression, bias amplification, near instruments, and measurement errors.