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An index of algorithms for learning causality with data

License: MIT License

causality causal-inference causality-analysis causality-algorithms unconfoundedness-assumption baselines awesome learning-to-rank recommender-system multilabel-classification

awesome-causality-algorithms's Introduction

Hi there ๐Ÿ‘‹

I am Ruocheng Guo, a ML Researcher working for ByteDance Research @ London, UK. I am working on causal ML, LLM alignment, and conformal prediction.

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davymorgan avatar howardhuang98 avatar jeankaddour avatar kimmo1019 avatar kunwuz avatar lfhase avatar liuzhaohan0 avatar maks-sh avatar maximilianfranz avatar millerjohnp avatar mschauer avatar paullo0106 avatar pei427 avatar rguo12 avatar rpryzant avatar samcarlos avatar suranah avatar usaito avatar valentyn1997 avatar yaochenzhu avatar

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awesome-causality-algorithms's Issues

Adding Causal Transformer from pre-print "Causal Transformer for Estimating Counterfactual Outcomes"

I wanted to modestly propose expanding "Learning Time Varying/Dependent Causal Effects" section with our pre-print "Causal Transformer for Estimating Counterfactual Outcomes".

The code (https://github.com/Valentyn1997/CausalTransformer) also contains the PyTorch-Lightning implementation of other baselines over time, e.g., CRN, RMSN or MSMs.

I can create a corresponding pull request if you don't mind.

Best regards,
Valentyn

References on cause-effect inference

Thank you for this great list!

Currently there is only one reference under "Distinguishing Cause from Effect (Bivariate)".
Below are some references published in recent 3 years (in no particular order).
I try to make a high-variance-low-bias list, keeping an eye on the diversity, without bias to a particular kind of methods.
Hope these help.

https://dl.acm.org/doi/abs/10.1145/3292500.3330854 (KDD19)
http://proceedings.mlr.press/v84/bloebaum18a.html (AISTATS18)
http://proceedings.mlr.press/v119/tagasovska20a.html (ICML20)
https://link.springer.com/chapter/10.1007/978-3-319-98131-4_3
https://proceedings.neurips.cc/paper/2018/hash/73fed7fd472e502d8908794430511f4d-Abstract.html
https://proceedings.neurips.cc/paper/2020/hash/a979ca2444b34449a2c80b012749e9cd-Abstract.html
https://arxiv.org/abs/2007.02809 (AAAI21)
http://proceedings.mlr.press/v108/wu20b.html (AISTATS20)
This last is my own work, and to the best of my knowledge, it still keeps the best result on benchmark dataset.
I will publish the code in the recent future.

Cheers,
Pengzhou (Abel)

paper released: Uber CausalML

I find CausalML is published with 2 pages in 2020.

@article{chen2020causalml,
  title={Causalml: Python package for causal machine learning},
  author={Chen, Huigang and Harinen, Totte and Lee, Jeong-Yoon and Yung, Mike and Zhao, Zhenyu},
  journal={arXiv preprint arXiv:2002.11631},
  year={2020}
}

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