Causal Analysis

Causal Inference and the Data-fusion Problem

DSA ADS Course - 2021

Causal Inference, Data-fusion, Causal Analysis, Causal Data Science, Counterfactuals, Selection Bias

Discuss concepts and techniques of different approaches to causal analysis, the curse of big data, data fusion and bias challenges. Discuss biases such as: confounding, sampling selection, and cross-population biases, along with a general, potential mitigation nonparametric framework for handling biases.

Discuss appropriate use of counterfactuals in causal analysis and risk of reasonable inference vs. unreasonable inference.

Causes of Effects and Effects of Causes



This paper summarizes methods that were found useful in estimating the probability that one event was a necessary cause of another, as interpreted by law makers. We show that the fusion of observational and experimental data can yield informative bounds which, under certain circumstances, meet legal criteria of causation. We further investigate the circumstances under which such bounds can emerge, and the philosophical dilemma associated with determining individual cases from statistical data.

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.