Contents
What is CausalImpact?
The CausalImpact package constructs a synthetic baseline for the post-intervention period based on a Bayesian structural time series model that incorporates multiple matching control markets as predictors, as well as other features of the time series.
Is Causal inference econometrics?
Causal inference is arguably one of the most important goals in applied econo- metric work. Construct- ing causal knowledge by uncovering quantitative relationships in statistical data is the goal of econometrics since the beginning of the discipline (Frisch, 1933).
How does Causal impact work?
Originally developed as an R package, Causal Impact works by fitting a Bayesian Structural Time Series (BSTS) model to a set of target and control time series observations, and subsequently performs posterior inference on the counterfactual.
What’s the difference between causal impact and synthetic control?
The difference between Synthetic Control and Causal Impact is that Synthetic Control uses only pre-treatment variables for matching while Causal Impact uses the full pre and post-treatment time series of predictor variables for matching.
How does synth predict the counter factual GDP?
The synth algorithm predicts the counter-factual by assigning weights to the regressors in the control groups which helps identify individual regressors and their influence in prediction. Ultimately, the true causal impact is the difference in GDP between actual GDP and the counter-factual GDP if the treatment had not happened.
Why do we use the causal impact method?
Causal Impact is a methodology developed by Google to estimate the causal impact of a treatment in the treated group. The official documentation can be found here. The motivation to use Causal Impact methodology is that the Difference in differences in limited in the following ways:
How is difference in differences used in causal inference?
The solution to this is to compare the trend to a control region which was not impacted by terrorist conflict. This comparison allows us to remove the confounding effect after the intervention period and arrive at the real causal impact. That’s where the Difference in differences method helps.