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- Article
The Importance of Being Causal
By: Iavor I Bojinov, Albert Chen and Min Liu
Causal inference is the study of how actions, interventions, or treatments affect outcomes of interest. The methods that have received the lion’s share of attention in the data science literature for establishing causation are variations of randomized experiments.... View Details
Keywords: Causal Inference; Observational Studies; Cross-sectional Studies; Panel Studies; Interrupted Time-series; Instrumental Variables
Bojinov, Iavor I., Albert Chen, and Min Liu. "The Importance of Being Causal." Harvard Data Science Review 2.3 (July 30, 2020).
- 04 Jun 2013
- First Look
First Look: June 4
industries and exploit cross-section and time-series variation in import tariffs to examine their impact on firm boundaries. Our empirical results provide strong support for the view that output prices are a key determinant of vertical... View Details
Keywords: Sean Silverthorne