Tutorial 3: Controlling for Text in Causal Inference with Double Machine Learning

Tutorial 3: Controlling for Text in Causal Inference with Double Machine Learning

NLP and CSS 201: Beyond the Basics

Establishing causal relationships is a fundamental goal of scientific research. Text plays an increasingly important role in the study of causal relationships across domains especially for observational (non-experimental) data. Specifically, text can serve as a valuable “control” to eliminate the effects of variables that threaten the validity of the causal inference process. But how does one control for text, an unstructured and nebulous quantity? In this tutorial, we will learn about bias from confounding, motivation for using text as a proxy for confounders, apply a “double machine learning” framework that uses text to remove confounding bias, and compare this framework with non-causal text dimensionality reduction alternatives such as topic modeling.