12 июн. 2023 г. · Yes, package used in this is tfcausalimpact which is Google CausalImpact rebuilt in Python on top of tensor flow probably.

25 июн. 2023 г. · 2.2K subscribers in the BayesianProgramming community. Bayesian programming is a formalism and a methodology to specify probabilistic models ...

11 июн. 2023 г. · Counterfactual Inference Using Time Series Data Sharing a machine learning (and causal inference) technique I think more data scientists ...

Original - Counterfactual Inference Using Time Series Data · Learn data science · More posts you may like · Top Posts ...

2 мар. 2021 г. · We observe the actual time series and then model the counterfactual time series that would have been, had something not occurred that did occur.

13 июн. 2021 г. · This paper introduces a unified framework of counterfactual estimation for time-series cross-sectional data, which estimates the average ...

14 окт. 2019 г. · I have recently discovered that, to determine causality in time series econometrics, one would have to use something called a "Granger causality" test.

3 янв. 2024 г. · Some of my colleagues do counterfactual analysis where we use time series analysis or synthetic controls to predict what the value would have ...

4 июл. 2023 г. · Counterfactual Inference Using Time Series Data. 5 upvotes. r ... r/learnmachinelearning - Data preprocessing for time-series forecasting using ...

1 авг. 2024 г. · Picking the 'right' causal design comes down to how the treatment is assigned and whether you have clearly defined treatment and control groups.