This interactive page replicates key results from the hypothetical marketing case study in Causal Inference in Python (Chapter 9: Synthetic Control). Utilizing the synthetic dataset meticulously crafted by Facure (2023) for this illustrative purpose, users can explore the effects of marketing strategies on app download trends in São Paulo.
The Synthetic Control Method (SCM) is a statistical technique to estimate the causal effect of a treatment, policy, or intervention on a particular unit (such as a country, region, or firm) by constructing a synthetic control group. This method is particularly useful if there is one (or just a few) treated units, and if it's not possible to rely on experimental designs.
Click on "Examples" (at the top of the page) to replicate results from other published works. Users can also upload their own datasets for custom analysis and generate insightful visual comparisons.
Special Predictors (0)
Evaluate the impact of an intervention
Understand the contribution of each control unit
Visualize the size of the causal effect
Compare average vs. aggregated impacts
Analyze the pre-treatment covariates
Find the statistical significance of the results
Test the validity of your results relative to time
Test the validity of your results among all controls
Customize and export your results
Share an interactive presentation with a link
Analyze your data faster and iterate efficiently
Meet CausalGPT: Causalify's AI assistant
Understand and interpret your results better
Access the World Bank's global data with ease
Access major country / state level sources