Mastering Causal Inference with Python: A Guide to Synthetic Control Groups

Author(s): Lukasz Szubelak

Originally published on Towards AI.

Photo by Isaac Smith on Unsplash

One can feel intrigued when a newspaper like the Washington Post writes an article about the statistical method. Statistical modeling isn’t usually the most exciting topic. However, in 2015 (yes, it was a long time ago), the Washington Post released an article describing the synthetic control group method. The fact that such a reputable source was discussing it hints at its importance. This article examines one of the most critical components of the causal inference arsenal: the synthetic control group.

This post is based on a seminal article by Alberto Abadie and Javier Gardeazabal, The Economic Costs of Conflict: A Case Study of the Basque Country. Their analysis not only examined the effect of terrorist activity on the economic development of the Basque Country but also set a new standard for causal inference research.

Terrorism activity in the Basque Country

Let’s start with a brief description of the analyzed problem. In the middle of the 1970s, the Basque Country, one of the Spanish regions, became affected by terrorist activity conducted by the separatist ETA group. The group wanted to gain independence from Spain for this region. The authors of the article mentioned above evaluated the effect of violence… Read the full blog for free on Medium.

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Published via Towards AI