11 июн. 2023 г. · In this article, we're going to take a deep dive into counterfactual inference using time series data. We'll start with a quick primer on causal inference.

24 нояб. 2024 г. · Counterfactual inference is a causal method, which deducts the target distribution given a change in the distribution of covariates, or derives ...

2 авг. 2022 г. · This paper introduces a simple framework of counterfactual estimation for causal inference with time-series cross-sectional data. Abstract · Verification Materials · Counterfactual Estimators

17 сент. 2024 г. · We provide here an R script to guide you through the execution and interpretation of the CausalArima and CausalImpact models, using simulated data.

12 июн. 2023 г. · Yes, package used in this is tfcausalimpact which is Google CausalImpact rebuilt in Python on top of tensor flow probably. Bayesian Counterfactual Inference Using Time Series Data [Q] Causal Inference using Classical Time Series models ... Другие результаты с сайта www.reddit.com

In this work, we propose a counterfactual based method to learn the importance of every observation in a multivariate time series model. We assign importance ...

Counterfactual Explanations for Time Series Models. There also works that generate counterfactual explanations for time series models. (Dhaou et al., 2021) ...

Some interpretation methods are specialized for time series data; these include perturbation-based (Pan et al., 2021), rule-based (Rajapak- sha & Bergmeir, 2022) ...

21 нояб. 2024 г. · This research proposes a global forecasting and inference method based on recurrent neural networks (RNN) to predict policy interventions' ...

This paper introduces a unified framework of counterfactual estimation for causal infer- ence with time-series cross-sectional data, in which we estimate the ...