Augmented Difference-in-Differences

We propose a new, easy-to-implement augmented difference-in-differences (ADID) method to estimate causal effects from quasi-experimental data.

• 저널Marketing Science · 42(4) · 746–767
• 토픽Advanced Causal Inference Techniques · Statistics and Probability
• DOI10.1287/mksc.2022.1406
• 저자Kathleen T. Li, Christophe Van den Bulte