PhD-RP

This is my reading list to organize my ideas to write my PhD research proposal.

Causal Discovery

1. Causal Discovery and Cross sectional data

1. Review

Eberhardt, F. (2016). Introduction to the foundations of causal discovery. International Journal of Data Science and Analytics.

Glymour, C. Zhang, Z. Spirtes, P. (2019). Review of Causal Discovery Methods Based on Graphical Models. Front. Gen.

Spirtes, P. Zhang, K. (2016). Causal discovery and inference: concepts and recent methodological advances. Applied Informatics.

2. Constraint-based Methods

a) PC Algorithm

Spirtes, P., Glymour, C., and Scheines, R. (2001). Causation, Prediction, and Search. MIT Press.

a) FCI Algorithm

Spirtes, P. Glymour, C. and Scheines, R. (2001). Causation, Prediction, and Search. MIT Press.

Colombo, D. Maathuis, M. H. Kalisch, M. Richardson, T. (2012). Learning high-dimensional directed acyclic graphs with latent and selection variables. Ann. Statist.

3. Score-based Methods

a) Greedy Equivalence Search

Chickering, D. M. (2003). Optimal structure identification with greedy search. J. Mach. Learn. Res.

4. Hybrid Methods

a) GFCI

Ogarrio, J. M. Spirtes, P. and Ramsey, J. (2016). A hybrid causalsearch algorithmfor latent variable models. JMLR Workshop and Conference Proceedings(International Conference on Probabilistic Graphical Models).

b) FRITL

Chen, W. et al. (2021). FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders. Arxiv.

5. Structural Causal Models (SEM)

a) ICA LiNGAM

Shimizu, S. Hoyer, P. Hyvärinen, A. Kerminen, A. (2006). A linear non-Gaussian acyclic model for causal discovery. J. Mach. Learn. Res.

b) Bayesian LiNGAM

Hoyer, P. Hyttinen, A. (2009). Bayesian Discovery of Linear Acyclic Causal Models. UAI.

b) ICA Lv-LiNGAM

Hoyer, P. O., Shimizu, S., Kerminen, A. J., and Palviainen, M. (2008). Estimation of causal effects using linear non-gaussian causal models with hidden variables. Int. J. Approx. Reason.

c) Non-linear Additive Models

Hoyer, P. Janzing, D. Mooji, J. Peters, J. Schölkopf, B. (2009). Non-linear causal discovery with additive noise models. Advances in Neural Information Processing Systems.

d) Non-stationary data

Huang, B. et al. (2020). Causal Discovery from Heterogeneous/Nonstationary Data. JMLR.

2. Causal Discovery and Time Series Process

1. Review

Eichler, M. (2012). Causal inference in time series analysis. Advances in Neural Information Processing Systems.

Runge, J. (2018). Causal network reconstruction from time series: From theoretical assumptions to practical estimation. Chaos.

2. SEM based - Econometrics

Granger, C. (1980). Testing for causality: a personal viewpoint. J Econ Dyn Control.

Sims, C. A. (1980). Macroeconomics and reality. Econometrica.

3. SEM based - CS

a) VAR-ICA LiNGAM

Hyvärinen, A. Zhang, K. Shimizu, S. Hoyer , P. (2010). Estimation of a structural vector autoregression model using non-gaussianity. J Machine Learn Res

b) Subsampled Time Series Process

Danks, D. Plis, S. (2014). Learning causal structure from undersampled time series. JMLR: Workshop and Conference Proceedings (NIPS Workshop on Causality)

Gong, M. Zhang, K. Schölkopf, B. Glymour, C. Tao, D. (2017). Causal Discovery from Temporally Aggregated Time Series. UAI.

c) Non-stationary Time Series

Huang, B. et al. (2019). Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models. PMLR.

c) Gaussiana Process

Huang, B. Zhang, K. Schölkopf, B. (2015). Identification of Time-Dependent Causal Model: A Gaussian Process Treatment. AAAI

4. Constraint-based Methods

Runge, J. (2018). Causal network reconstruction from time series: From theoretical assumptions to practical estimation. Chaos.

Gerhardus, A. Runge, J. (2021). High-recall causal discovery for autocorrelated time series with latent confounders. Conference on Neural Information Processing Systems.

Out-of-distribution Generalization