Causal Analysis

Causality Models, Reasoning and Inference: Causal Data Science Course

DSA ADS Course - 2021

Causal Analysis, Causality Models, Causal Inference

Discuss causal analysis and how it applies to many different fields like health policy and medical practice, public policy, business decisions, statistics, artificial intelligence, economics, philosophy, cognitive science, and others. Review probabilistic, manipulative, counterfactual, and structural approaches to causation and apply simple mathematical tools for studying the relationships between causal connections and statistical associations.

Probabilities of Causation: Three Counterfactual Interpretations and their Identification

DSA ADS Course - 2021

Causal Analysis, Causality Inference, Counterfactuals

Discuss how data from both experimental and nonexperimental studies combined provide information that neither study alone can provide. Show that necessity and sufficiency are two independent aspects of causation, and that both should be invoked in the construction of causal explanations for specific scenarios.

1999 - Probabilities of Causation: Three Counterfactual Interpretations and their Identification by Judea Pearl


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.