CausalImpact <img class="emoji" alt="heart" src="https://cdn.jsdelivr.net/gh/qinwf/awesome-R@3c66da6e291bcc0520b1649125b0bed750896a9a/heart.png" height="20" align="absmiddle" width="20">
An R package for estimating causal effects in time series using Bayesian structural time-series models.
What is CausalImpact <img class="emoji" alt="heart" src="https://cdn.jsdelivr.net/gh/qinwf/awesome-R@3c66da6e291bcc0520b1649125b0bed750896a9a/heart.png" height="20" align="absmiddle" width="20">?
CausalImpact is an R package for estimating the causal effect of an intervention on a time series using Bayesian structural time-series models. It helps answer questions like how many additional daily clicks were generated by an advertising campaign when randomized experiments are not available. The package models how the response metric might have evolved without the intervention, providing data-driven insights for decision-making.
Data scientists, statisticians, and analysts working in marketing, policy evaluation, or business intelligence who need to estimate causal effects from observational time-series data.
Developers choose CausalImpact for its robust Bayesian methodology, ability to handle non-experimental data, and comprehensive output that includes uncertainty quantification. It is backed by Google research and offers a principled approach to causal inference in time series.
Overview
An R package for causal inference in time series
Use Cases
Best For
- Measuring the impact of marketing campaigns on website traffic or sales
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