Introduction to doubleml and causalml: Machine Learning Meets Causal Inference

📰 Medium · Machine Learning

Learn how to apply machine learning to causal inference using doubleml and causalml libraries in Python, enabling more robust causal reasoning with large numbers of covariates.

intermediate Published 14 Apr 2026

Action Steps

Import the doubleml library and use its functionality to estimate causal effects with machine learning models.
Use causalml to apply various causal inference methods, such as instrumental variables and regression discontinuity design.
Compare the results of traditional econometric methods with those obtained using doubleml and causalml to evaluate their performance.
Apply doubleml and causalml to a real-world dataset to estimate causal effects and validate the results using placebo tests.
Evaluate the robustness of the results to different machine learning models and hyperparameters.

Who Needs to Know This

Data scientists and analysts on a team can benefit from using doubleml and causalml to improve the accuracy of their causal inference models, especially when dealing with complex data sets.

Key Insight

💡 Doubleml and causalml libraries can be used to combine the strengths of machine learning and traditional econometric methods for more accurate causal inference.