Examples#
Python: Case studies#
These are case studies with the Python package DoubleML.
General Examples#
Python: Basics of Double Machine Learning
Python: Impact of 401(k) on Financial Wealth
Python: Sensitivity Analysis
Python: Average Potential Outcome (APO) Models
Python: Choice of learners
Python: First Stage and Causal Estimation
Python: Cluster Robust Double Machine Learning
Python: Sample Selection Models
Example: Sensitivity Analysis for Causal ML
Python: Difference-in-Differences
Python: Difference-in-Differences Pre-Testing
Python: Basic Instrumental Variables calculation
Python: PLM and IRM for Multiple Treatments
DoubleML meets FLAML - How to tune learners automatically within DoubleML
Effect Heterogeneity#
Python: Group Average Treatment Effects (GATEs) for IRM models
Python: Group Average Treatment Effects (GATEs) for PLR models
Python: Conditional Average Treatment Effects (CATEs) for IRM models
Python: Conditional Average Treatment Effects (CATEs) for PLR models
Python: GATE Sensitivity Analysis
Python: Policy Learning with Trees
Python: Impact of 401(k) on Financial Wealth (Quantile Effects)
Python: Potential Quantiles and Quantile Treatment Effects
Python: Conditional Value at Risk of potential outcomes
R: Case studies#
These are case studies with the R package DoubleML.
Sandbox#
These are examples which are work-in-progress and/or not yet fully documented.
