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We discuss recent work for causal inference and predictive robustness in a unifying way.
On the application of probability theory to agricultural experiments. Essay on principles. Section 9. Translated and edited by D.M. Dabrowska and T.P. Speed from the Polish original, which appeared in Roczniki Nauk Rolniczyc, Tom X (1923): 1–51 (Annals of Agricultural Sciences)
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The statistical implications of a system of simultaneous equations
Haavelmo, T. (1943) · 1943
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Tests of equality between sets of coefficients in two linear regressions
Chow, G. C. (1960) · 1960
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Instrumental Variables
Bowden, R. and Turkington, D. (1990) · 1990
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Discriminability-based transfer between neural networks
Pratt, L. Y. (1993) · 1993
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Identification of causal effects using instrumental variables
Angrist, J., Imbens, G., and Rubin, D. (1996) · 1996
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Regression shrinkage and selection via the Lasso
Tibshirani, R. (1996) · 1996
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Prediction games and arcing algorithms
Breiman, L. (1999) · 1999
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Causal diagrams for epidemiologic research
Greenland, S., Pearl, J., and Robins, J. M. (1999) · 1999
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Causal inference without counterfactuals
Dawid, A. P. (2000) · 2000
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Marginal structural models and causal inference in epidemiology
Robins, J. M., Hernán, M. A., and Brumback, B. (2000) · 2000
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Causation, Prediction, and Search
Spirtes, P., Glymour, C., and Scheines, R. (2000) · 2000
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Random forests
Breiman, L. (2001) · 2001
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Greedy function approximation: a gradient boosting machine
Friedman, J. (2001) · 2001
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Optimal structure identification with greedy search
Chickering, D. (2002) · 2002
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Ancestral graph markov models
Richardson, T., Spirtes, P., et al. (2002) · 2002
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A survey of weak instruments and weak identification in generalized method of moments
Stock, J. H., Wright, J. H., and Yogo, M. (2002) · 2002
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Boosting with the L 2 L_{2} loss: regression and classification
Bühlmann, P. and Yu, B. (2003) · 2003
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Estimating causal effects from epidemiological data
Hernán, M. A. and Robins, J. M. (2006) · 2006
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Avoiding invalid instruments and coping with weak instruments
Murray, M. P. (2006) · 2006
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Boosting algorithms: regularization, prediction and model fitting (with discussion)
Bühlmann, P. and Hothorn, T. (2007) · 2007
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Estimating high-dimensional directed acyclic graphs with the PC-algorithm
Kalisch, M. and Bühlmann, P. (2007) · 2007
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Nonlinear causal discovery with additive noise models
Hoyer, P., Janzing, D., Mooij, J., Peters, J., and Schölkopf, B. (2009) · 2008
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Estimating high-dimensional intervention effects from observational data
Maathuis, M., Kalisch, M., and Bühlmann, P. (2009) · 2009
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Causality: Models, Reasoning and Inference
Pearl, J. (2009) · 2009
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Jointly interventional and observational data: estimation of interventional markov equivalence classes of directed acyclic graphs
Hauser, A. and Bühlmann, P. (2015) · 2015
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Causal Inference for Statistics, Social, and Biomedical Sciences
Rubin, D. and Imbens, G. (2015) · 2015
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Explanation in Causal Inference: Methods for Mediation and Interaction
VanderWeele, T. (2015) · 2015
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Instrumental variables estimation with some invalid instruments and its application to Mendelian randomization
Kang, H., Zhang, A., Cai, T. T., and Small, D. S. (2016) · 2016
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Methods for causal inference from gene perturbation experiments and validation
Meinshausen, N., Hauser, A., Mooij, J., Peters, J., Versteeg, P., and Bühlmann, P. (2016) · 2016
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Cause and effect
Editorial (2010) · 2010
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Causal Inference
Hernán, M. A. and Robins, J. M. (2010) · 2010
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Predicting causal effects in large-scale systems from observational data
Maathuis, M., Colombo, D., Kalisch, M., and Bühlmann, P. (2010) · 2010
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Stability selection (with discussion)
Meinshausen, N. and Bühlmann, P. (2010) · 2010
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A survey on transfer learning
Pan, S. J. and Yang, Q. (2010) · 2010
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Statistics for High-Dimensional Data: Methods, Theory and Applications
Bühlmann, P. and van de Geer, S. (2011) · 2011
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Causal inference using invariant prediction: identification and confidence interval (with discussion)
Peters, J., Bühlmann, P., and Meinshausen, N. (2016) · 2016
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Wasserstein distributional robustness and regularization in statistical learning
Gao, R., Chen, X., and Kleywegt, A. J. (2017) · 2017
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Conditional variance penalties and domain shift robustness
Heinze-Deml, C. and Meinshausen, N. (2017) · 2017
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nonlinearICP: Invariant Causal Prediction for Nonlinear Models
Heinze-Deml, C. and Peters, J. (2017) · 2017
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seqICP: Sequential Invariant Causal Prediction
Pfister, N. and Peters, J. (2017) · 2017
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Rothenhäusler, D., Bühlmann, P., and Meinshausen, N. (2017) · 2017
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Confidence intervals for causal effects with invalid instruments by using two-stage hard thresholding with voting
Guo, Z., Kang, H., Tony Cai, T., and Small, D. S. (2018) · 2018
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Invariant causal prediction for nonlinear models
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Causality from a distributional robustness point of view
Meinshausen, N. (2018a) · 2018
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Invariant causal prediction for sequential data
Pfister, N., Bühlmann, P., and Peters, J. (2018) · 2018
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Invariant models for causal transfer learning
Rojas-Carulla, M., Schölkopf, B., Turner, R., and Peters, J. (2018) · 2018
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Anchor regression: heterogeneous data meets causality
Rothenhäusler, D., Meinshausen, N., Bühlmann, P., and Peters, J. (2018) · 2018
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Certifiable distributional robustness with principled adversarial training
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A linear non-Gaussian acyclic model for causal discovery
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