Causal Inference and Graphical Representations

I’m taking a course on causal inference in the Department of Sociology at JHU. The book we’re using (Morgan and Winship) focuses on the approach to causal inference that lies at the intersection of the potential outcome model and the causal graph work that’s been pushed forward by Judea Pearl in the pat 20 years or so.

In the first chapter, Morgan and Winship lay out the three main approaches they go over in the book: conditioning arguments (removing “backdoor” effects), instrumental variables and mechanism models. I don’t fully understand the math behind these strategies, but when reading the high-level descriptions I couldn’t help but be reminded of the three different types of reasoning expressed in graphical models: causal (not used in the same was as in causal inference), evidential, and co-causal or “explaining away.” I’m looking forward to fleshing these analogies out once we’ve made it further through the book.

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