Causal Effect Estimation
Learn to estimate causal effects from observational data using techniques like propensity score matching, inverse probability weighting, instrumental variables, and double machine learning, enabling you to go beyond correlation and understand true causal impacts.
5 Steps
- 1
Introduction to Causal Inference: Understand the fundamental difference between correlation and causation. Learn about potential outcomes framework and the concept of Average Treatment Effect (ATE).
- 2
Propensity Score Matching: Implement propensity score matching to create comparable treatment and control groups. Use logistic regression to estimate propensity scores and match treated units to control units based on these scores.
- 3
Inverse Probability Weighting (IPW): Apply inverse probability weighting to adjust for confounding. Calculate weights based on propensity scores and use them to estimate the ATE.
- 4
Instrumental Variables (IV): Explore the use of instrumental variables to address confounding when other methods are insufficient. Identify a valid instrument and perform two-stage least squares regression.
- 5
Double Machine Learning (DML): Implement double machine learning to estimate the ATE using machine learning models to predict both the treatment and the outcome. This method is robust to model misspecification.
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