GitHub

Causal Inference and Discovery in Python

What is this book about? Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that ...
eLife

Exploiting fluctuations in gene expression to detect causal interactions between genes

Analyzing stochastic cell-to-cell variability can potentially reveal causal interactions in gene regulatory networks.
GitHub

CausalInferenceLab/awesome-causal-inference-python

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IEEE

Causal Intervention Is What Large Language Models Need for Spatio-Temporal Forecasting

Abstract: Spatio-temporal forecasting plays a crucial role in the dynamic perception of smart cities, such as traffic flow prediction, renewable energy forecasting, and load prediction. Its objective ...
IEEE

Interventional and Counterfactual Inference with Autoregressive Flow Models

Abstract: Causal inference plays a crucial role in data science applications. To address the issue of estimating the distributions of observational, interventional, and counterfactual data. We present ...