Python Library for learning (Structure and Parameter) and inference (Statistical and Causal) in Bayesian Networks.
pgmpy pgmpy is a python library for working with Probabilistic Graphical Models. Documentation and list of algorithms supported is at our official sit
Code for Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding
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CausalNLP is a practical toolkit for causal inference with text as treatment, outcome, or "controlled-for" variable.
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CausaLM: Causal Model Explanation Through Counterfactual Language Models
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Deep Learning Models for Causal Inference
Extensive tutorials for learning how to build deep learning models for causal inference using selection on observables in Tensorflow 2.
Code for "Causal autoregressive flows" - AISTATS, 2021
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[ICCV 2021] Released code for Causal Attention for Unbiased Visual Recognition
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Causal estimators for use with WhyNot
WhyNot Estimators A collection of causal inference estimators implemented in Python and R to pair with the Python causal inference library whynot. For
Multi-task Learning of Order-Consistent Causal Graphs (NeuRIPs 2021)
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