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.
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How To Use The Synthetic Control Method in R Step-By-Step: Effect of California's Tobacco Control Program. #SyntheticControl #CausalInference #DataScience #Economics #ImpactEvaluation #Statistics youtu.be/xCNQdnZzg64?si…