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.

The Causal Impact model developed by Google works by fitting a Bayesian structural time series model to observed data which is later used for predicting ...

TFP CausalImpact. This Python package implements an approach to estimating the causal effect of a designed intervention on a time series.

Python version of Google's Causal Impact model on top of Tensorflow Probability. copied from cf-staging / tfcausalimpact.

Библиотека для поиска причинно-следственных связей на Python, основанная на пакете R от Google. Построена с использованием TensorFlow Probability.

18 дек. 2020 г. · Introduction to tfcausalimpact built on top of Python. Examples. We'll be able to fully analyze whether a given random variable causes impact on ...

24 янв. 2024 г. · I know that I can access the p-value by instantiating the model ci = CausalImpact(data,pre_period,post_period) ci.summary() But that gives a wrapped-up text ...

## How It Works The algorithm basically fits a [Bayesian structural](https://en.wikipedia.org/wiki/Bayesian_structural_time_series) model on past observed data ...

We also created this introductory [ipython notebook](https://github.com/WillianFuks/tfcausalimpact/blob/master/notebooks/getting_started.ipynb) with examples of ...

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