Collection of open machine learning papers

**Overview**- Causal inference in statistics:An overview (2009)
*Judea Pearl* - An Introduction to Causal Inference (2010)
*Judea Pearl* - Causality and statistical learning (2011)
*Andrew Gelman* - Causal discovery and inference: concepts and recent methodological advances (2016)
*Peter Spirtes, Kun Zhang* - A Survey of Learning Causality with Data: Problems and Methods (2018)
*Ruocheng Guo, Lu Cheng, Jundong Li, P. Richard Hahn, Huan Liu* - Statistical Tools for Causal Inference (2020)
*The SKY Community* - The fundamental problem of causal inference (Wiki)

- Causal inference in statistics:An overview (2009)

- On the application of probability theory to agricultural experiments (1923)
*Jerzy Neyman* - Estimating causal effects of treatments in randomized and nonrandomized studies (1974)
*Donald B. Rubin* - Causal Inference Using Potential Outcomes:Design, Modeling, Decisions (2005)
*Donald B. Rubin*

**PC**Peter-Clark Algorithm- Copula PC Algorithm for Causal Discoveryfrom Mixed Data (2016)
*Ruifei Cui, Perry Groot, Tom Heskes*

- Copula PC Algorithm for Causal Discoveryfrom Mixed Data (2016)
**FCI**Fast Causal Inference- Causal Inference in the Presence of Latent Variables and Selection Bias (1995)
*Peter Spirtes, Christopher Meek, Thomas Richardson* - Search for Additive Nonlinear TimeSeries Causal Models (2008)
*Tianjiao Chu, Clark Glymour*

- Causal Inference in the Presence of Latent Variables and Selection Bias (1995)
**GES**Greedy Equivalence Search- Optimal Structure Identification With Greedy Search (2002)
*David Maxwell Chickering*

- Optimal Structure Identification With Greedy Search (2002)
**FCM**Functional Causal Models**ANM**Additive Noise Models- Causal discovery of linear acyclic models with arbitrary distributions (2008)
*Patrik O. Hoyer, Aapo Hyvarinen, Richard Scheines, Peter Spirtes, Joseph Ramsey, Gustavo Lacerda, Shohei Shimizu* - Nonlinear causal discovery with additive noise models (2009)
*Patrik O. Hoyer, Dominik Janzing, Joris M. Mooij, Jonas Peters, Bernhard SchĂ¶lkop*

- Causal discovery of linear acyclic models with arbitrary distributions (2008)
**Granger causality**- Investigating Causal Relations by Econometric Models and Cross-spectral Methods (1969)
*C. W. J. Granger* - Aalyzing multiple nonlinear time series with extended Granger causality (2004)
*Yonghong Chena, Govindan Rangarajanc, Jianfeng Fenge, Mingzhou Ding* - Large-scale nonlinear Granger causality: A data-driven, multivariateapproach to recovering directed networks from short time-series data (2020)
*Axel Wismuller, Adora M. DSouza, Anas Z. Abidin*

- Investigating Causal Relations by Econometric Models and Cross-spectral Methods (1969)

- Python
- tigramite (Homepage)