References#
Joshua D Angrist and Guido W Imbens. Two-stage least squares estimation of average causal effects in models with variable treatment intensity. Journal of the American statistical Association, 90(430):431–442, 1995.
Joshua D Angrist, Guido W Imbens, and Donald B Rubin. Identification of causal effects using instrumental variables. Journal of the American statistical Association, 91(434):444–455, 1996.
Peter C Austin. A comparison of 12 algorithms for matching on the propensity score. Statistics in medicine, 33(6):1057–1069, 2014.
James Lopez Bernal, Steven Cummins, and Antonio Gasparrini. Interrupted time series regression for the evaluation of public health interventions: a tutorial. International journal of epidemiology, 46(1):348–355, 2017.
Kenneth A. Bollen. Structural Equations with Latent Variables. John Wiley & Sons, 1989.
Derek W Bunn. Modelling Prices in Competitive Electricity Markets. John Wiley & Sons, 2004.
Derek W Bunn, John N Inekwe, and David MacGeehan. Analysis of the fundamental predictability of prices in the british balancing market. IEEE Transactions on Power Systems, 36(2):1309–1316, 2020.
Marco Caliendo and Sabine Kopeinig. Some practical guidance for the implementation of propensity score matching. Journal of economic surveys, 22(1):31–72, 2008.
Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, and James Robins. Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1):C1–C68, 2018.
David Maxwell Chickering. Optimal structure identification with greedy search. Journal of Machine Learning Research, 3:507–554, 2002.
Stephen G Donald and Kevin Lang. Inference with difference-in-differences and other panel data. The review of Economics and Statistics, 89(2):221–233, 2007.
Ronald A Fisher. The design of experiments. Oliver And Boyd; Edinburgh, 1935.
Jonathan Fuhr, Philipp Berens, and Dominik Papies. Estimating causal effects with double machine learning–a method evaluation. arXiv preprint arXiv:2403.14385, 2024.
Clive W. J. Granger. Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3):424–438, 1969.
Miguel A Hernan and James M Robins. Causal Inference: What If. Chapman & Hall/CRC, 2020.
Aapo Hyvärinen and Erkki Oja. Independent component analysis: algorithms and applications. Neural networks, 13(4-5):411–430, 2000.
Aapo Hyvärinen, Shohei Shimizu, and Patrik O Hoyer. Causal modelling combining instantaneous and lagged effects: an identifiable model based on non-gaussianity. In Proceedings of the 25th international conference on Machine learning, 424–431. 2008.
Aapo Hyvärinen, Kun Zhang, Shohei Shimizu, and Patrik O Hoyer. Estimation of a structural vector autoregression model using non-gaussianity. Journal of Machine Learning Research, 2010.
Takashi Ikeuchi, Mayumi Ide, Yan Zeng, Takashi Nicholas Maeda, and Shohei Shimizu. Python package for causal discovery based on lingam. Journal of Machine Learning Research, 24(14):1–8, 2023.
Daniel S Kirschen and Goran Strbac. Fundamentals of power system economics. John Wiley & Sons, 2018.
Daphne Koller and Nir Friedman. Probabilistic Graphical Models: Principles and Techniques. MIT Press, 2009.
Steffen L Lauritzen. Graphical Models. Oxford University Press, 1996.
Scott M Lundberg and Su-In Lee. A unified approach to interpreting model predictions. Advances in neural information processing systems, 2017.
Helmut Lütkepohl. Macroeconometrics and Time Series Analysis (Impulse response function Chapter). Springer, 2010.
Juan M Morales, Antonio J Conejo, Henrik Madsen, Pierre Pinson, and Marco Zugno. Integrating renewables in electricity markets: operational problems. Volume 205. Springer Science & Business Media, 2013.
Jerzy Neyman. On the application of probability theory to agricultural experiments. essay on principles. Statistical Science, 5(4):465–472, 1990. Translated and edited by DM Dabrowska and TP Speed.
Judea Pearl. Causality: Models, Reasoning, and Inference. Cambridge University Press, 2000.
Judea Pearl. Causal inference: history, perspectives, adventures, and unification (an interview with judea pearl). Observational Studies, 8(2):23–36, 2022.
Judea Pearl and Dana Mackenzie. The Book of Why: The New Science of Cause and Effect. Basic Books, 2018.
Egon S Pearson. On the use and interpretation of certain test criteria for purposes of statistical inference. Biometrika, 20(1/2):175–240, 1933.
Jonas Peters, Joris M Mooij, Dominik Janzing, and Bernhard Schölkopf. Causal discovery with continuous additive noise models. Journal of Machine Learning Research, 2014.
Donald B Rubin. Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of educational Psychology, 66(5):688, 1974.
Burr Settles. Active learning literature survey. Technical Report, 2009.
Shohei Shimizu, Patrik O Hoyer, Aapo Hyvärinen, Antti Kerminen, and Michael Jordan. A linear non-gaussian acyclic model for causal discovery. Journal of Machine Learning Research, 2006.
Aleksandrs Slivkins and others. Introduction to multi-armed bandits. Foundations and Trends® in Machine Learning, 12(1-2):1–286, 2019.
Peter Spirtes, Clark Glymour, and Richard Scheines. Causation, prediction, and search. MIT press, 2001.
Eyal Winter. The shapley value. Handbook of game theory with economic applications, 3:2025–2054, 2002.
Jinsung Yoon, James Jordon, and Mihaela Van Der Schaar. Ganite: estimation of individualized treatment effects using generative adversarial nets. In International conference on learning representations. 2018.