This R package implements an approach to estimating the causal effect of a designed intervention on a time series. For example, how many additional daily ...

TFP CausalImpact is a Python + TensorFlow Probability implementation of the CausalImpact R package developed at Google by Kay Brodersen and Alain Hauser. TFP ...

What does the package do? This R package implements an approach to estimating the causal effect of a designed intervention on a time series.

How It Works. The algorithm basically fits a Bayesian structural model on past observed data to make predictions on what future data would look like.

An R package for causal inference in time series. Contribute to google/CausalImpact development by creating an account on GitHub.

An R package for causal inference in time series. Contribute to google/CausalImpact development by creating an account on GitHub.

Causal inference using Bayesian structural time-series models. This package aims at defining a python equivalent of the R CausalImpact package by Google.

Python port of CausalImpact R library. Contribute to jamalsenouci/causalimpact development by creating an account on GitHub.

An R package for causal inference in time series. Contribute to google/CausalImpact development by creating an account on GitHub.

The TFP CausalImpact Authors attempt to fix plots so they will be visible in GitHub. last year History 589 lines (589 loc) 159 KB