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
11.73 score 1.7k stars 2 dependents 276 scripts 6.7k downloadspatrick - 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 21 days ago
6.92 score 139 stars 19 scripts 5.2k downloadsbsynth - 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 13 days ago
cpp
6.03 score 18 stars 4 scripts 261 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 6 months ago
cpp
3.88 score 3 stars 6 scripts 201 downloads