CausalImpact - Inferring Causal Effects using Bayesian Structural Time-Series Models

Implements a Bayesian approach to causal impact estimation in time series, as described in Brodersen et al. (2015) <DOI:10.1214/14-AOAS788>. See the package documentation on GitHub <https://google.github.io/CausalImpact/> to get started.

Last updated 2 years ago

11.77 score 1.7k stars 2 packages 278 scripts 7.5k downloads

patrick - Parameterized Unit Testing

This is an extension of the 'testthat' package that lets you add parameters to your unit tests. Parameterized unit tests are often easier to read and more reliable, since they follow the DNRY (do not repeat yourself) rule.

Last updated 10 months ago

6.04 score 135 stars 18 scripts 3.0k downloads

bsynth - Bayesian Synthetic Control

Implements the Bayesian Synthetic Control method for causal inference in comparative case studies. This package provides tools for estimating treatment effects in settings with a single treated unit and multiple control units, allowing for uncertainty quantification and flexible modeling of time-varying effects. The methodology is based on the paper by Vives and Martinez (2022) <doi:10.48550/arXiv.2206.01779>.

Last updated 5 months ago

5.68 score 16 stars 4 scripts 177 downloads

imt - Impact Measurement Toolkit

A toolkit for causal inference in experimental and observational studies. Implements various simple Bayesian models including linear, negative binomial, and logistic regression for impact estimation. Provides functionality for randomization and checking baseline equivalence in experimental designs. The package aims to simplify the process of impact measurement for researchers and analysts across different fields. Examples and detailed usage instructions are available at <https://book.martinez.fyi>.

Last updated 2 months ago

3.88 score 3 stars 6 scripts 147 downloads