30 сент. 2023 г. · Extensive tutorials for learning how to build deep learning models for causal inference (HTE) using selection on observables in Tensorflow 2 ...

18 дек. 2020 г. · In essence, it works by using latent variables that helps to describe the observed data. Such variables evolve through time by following certain ...

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

Abstract. This primer systematizes the emerging literature on causal inference using deep neural net- works under the potential outcomes framework.

29 мар. 2024 г. · This paper proposes to infer causal impact on the basis of a diffusion-regression state-space model that predicts the counterfactual market ...

9 сент. 2023 г. · Causal inference is one of the hallmarks of human intelligence. Corr2cause is a large-scale dataset of more than 400K samples, on which ...

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

18 дек. 2020 г. · This new open sourced repository ports the original R package CausalImpact to the Python language, running on top of TensorFlow Probability.

TensorFlow-Based Bayesian Causal Impact Analysis: Unveiling the Effects of a ... Causal inference in marketing: A review and suggestions for best practice.

This tutorial presents a walk-through on using DoWhy+EconML libraries for causal inference. Along the way, we'll highlight the connections to machine learning.