rguo12 / awesome-causality-algorithms

An index of algorithms for learning causality with data

Hi there 👋

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

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

A Survey on Causal Inference

Hi,
I find one comprehensive survey paper about Potential Outcome Framework. Link is attached here.
https://arxiv.org/abs/2002.02770

Adding X-learner framework

Hey :)

I wanted to propose adding the X-learner meta-learner framework for heterogeneous treatment effects.

Paper: https://www.pnas.org/content/pnas/116/10/4156.full.pdf
Code: https://github.com/xnie/rlearner/blob/master/R/xlearner.R

I guess this belongs to the i.i.d data section.

Thanks for the awesome collection and the comprehensive review paper, great work!

Add a new work on estimating causal effects under confounding

Shamelessly recommend my own preprint:
https://arxiv.org/abs/2101.06662
Some highlights: https://twitter.com/PengzhouW/status/1362225064309972993
In particular, it shows SOTA performance of balancing and deconfounding on IHDP and Pokec.

I do not know if preprints are allowed by the policy of your list. Simply ignore this if not.

Causal Structure Learning when Data is not Independent and Identically Distributed (non-IID)

treatment and outcome should be causal inference. There are many more non-iid related algorithms in causal discovery, such as those mentioned in this post https://sadhik9.people.uic.edu/shishir_wcp20.pdf, and I wonder if you'll be adding to this section subsequently.

Adding DAG and TCDF frameworks?

Hi,

Would the following frameworks work for this repo?

Timeseries non-i.d.d. data

• TCDF: Temporal Causal Discovery Framework (https://github.com/M-Nauta/TCDF)

Structured Learning i.d.d. data

• DAG-GNN (https://github.com/fishmoon1234/DAG-GNN)
• DAGs with NO TEARS (https://github.com/xunzheng/notears)

Thanks!

Add paper to NLP section

Hi! I'm not sure if I'm supposed to fork the repo or just leave a comment, but do you mind adding this paper to the NLP section:

https://www.aclweb.org/anthology/2020.acl-main.474.pdf (ACL2020)

Thanks!

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}
}