Must-read papers and resources related to causal inference and machine (deep) learning
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Updated
Nov 23, 2022
Must-read papers and resources related to causal inference and machine (deep) learning
Implementations and examples of common offline policy evaluation methods in Python.
"CoPhy: Counterfactual Learning of Physical Dynamics", F. Baradel, N. Neverova, J. Mille, G. Mori, C. Wolf, ICLR'2020
Pytorch Implementation of Counterfactual Recurrent Neural Network
https://arxiv.org/abs/2102.07355 [WACV Workshops 2022]
PokeMind: replay-driven AI research for strategic Pokémon card battles, counterfactual search, and conservative policy evaluation.
A research lab for off-policy evaluation, exploration, and policy-generated bias in contextual-bandit recommendation systems.
Fixed-anchor counterfactual consistency training and causal evidence auditing for chromatographic CNNs
Counterfactual crack synthesis and hard-negative learning for cross-domain infrastructure crack segmentation.
Counterfactual multi-branch decision collection and reward tooling for CARLA cooperative VLA research
Counterfactual learning-to-rank for marketplace search logs in PySpark: position-bias estimation, IPS-weighted training, NDCG evaluation against known ground truth
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