Learning Resource: Causal Machine Learning with DoubleML

Here are some study notes for Double Machine Learning in causal inference.

Introduction

Double machine learning, as introduced by (Chernozhukov et al. 2018), is a methodology used in causal inference, which is particularly useful when dealing with high-dimensional data.

1. The Challenge in High-Dimensional Data: In causal inference, we often want to estimate the effect of a particular variable (the treatment) on an outcome. However, in high-dimensional settings, where we have a large number of potential control variables (features), traditional methods can be inefficient or biased.

2. Concept of Double Machine Learning: The term “double” refers to a two-step process. The first step is to use machine learning methods to predict both the treatment and the outcome based on the control variables. The second step is to use these predictions to correct the bias in estimating the causal effect.

3. First Step – Machine Learning Models: Here, we use machine learning algorithms to model the relationship between the control variables and (a) the treatment, and (b) the outcome. This helps in understanding how these control variables influence both the treatment and the outcome separately.

4. Second Step – Causal Effect Estimation: After we’ve modeled the treatment and outcome separately, we can now adjust our estimation of the causal effect. This adjustment is crucial because it accounts for the influence of the control variables, reducing the risk of bias that could be introduced if these variables were ignored or improperly handled.

5. Orthogonality Principle: A key aspect of double machine learning is the orthogonality principle. It ensures that the estimation of the causal effect is not heavily influenced by small changes in the estimation of the control variables. This makes the method robust to the choice of machine learning models used in the first step.

6. Advantage in Real-world Applications: In practical scenarios, especially in marketing and economics, where datasets are large and complex, double machine learning offers a more reliable way to discern causal relationships, as it efficiently handles a large number of variables and complex relationships between them.

In summary, double machine learning in causal inference provides a robust and efficient way to estimate causal effects in high-dimensional settings by leveraging the power of machine learning algorithms to control for a large set of variables, thus reducing bias and increasing the reliability of causal estimations.

Learning Resource

This website for the “Tools for Causality - Double Machine Learning” course offers materials on Double Machine Learning, causal machine learning, heterogeneous treatment effects, sensitivity analysis, and advanced methods like Difference-in-Differences.

• There is also a helpful tutorial on YouTube, check here.

• Some useful slides they provided are the following:

Another similar Good Tutorial: DoubleML — DoubleML documentation.

Reference