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Estimation of causal effects involves crucial assumptions about the data-generating process, such as directionality of effect, presence of instrumental variables or mediators, and whether all relevant confounders are observed.
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Causality
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Zhang, K. and Hyvärinen, A · 2009
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Zhang, K. and Hyvärinen, A · 2010
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Pearl, J · 2012
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Experiment selection for causal discovery
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Robust causal inference using directed acyclic graphs: the r package ‘dagitty’
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Bayesian inference of individualized treatment effects using multi-task gaussian processes
Alaa, A. M. and van der Schaar, M · 2017
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Double/debiased/neyman machine learning of treatment effects
Chernozhukov, V., Chetverikov, D., 19 Demirer, M., Duflo, E., Hansen, C., and Newey, W · 2017
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Deep IV: A flexible approach for counterfactual prediction
Hartford, J., Lewis, G., Leyton-Brown, K., and Taddy, M · 2017
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Elements of causal inference: foundations and learning algorithms
Peters, J., Janzing, D., and Schölkopf, B · 2017
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Estimating individual treatment effect: generalization bounds and algorithms
Shalit, U., Johansson, F. D., and Sontag, D · 2017
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Machine teaching: A new paradigm for building machine learning systems
Simard, P. Y., Amershi, S., Chickering, D. M., Pelton, A. E., Ghorashi, S., Meek, C., Ramos, G., Suh, J., Verwey, J., Wang, M., et al · 2017
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Tikka, S. and Karvanen, J · 2017
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Adversarial generalized method of moments
Lewis, G. and Syrgkanis, V · 2018
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Syrgkanis, V., Lei, V., Oprescu, M., Hei, M., Battocchi, K., and Lewis, G · 2019
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Ananke: A module for causal inference
Bhattacharya, R., Lee, J., and Nabi, R · 2020
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Causalml: Python package for causal machine learning, 2020
Chen, H., Harinen, T., Lee, J.-Y., Yung, M., and Zhao, Z · 2020
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Cinelli, C. and Hazlett, C · 2020
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Counterfactual generation and fairness evaluation using adversarially learned inference
Dash, S., Balasubramanian, V., and Sharma, A · 2020
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Minimax estimation of conditional moment models
Dikkala, N., Lewis, G., Mackey, L., and Syrgkanis, V · 2020
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Some methods for heterogeneous treatment effect estimation in high dimensions
Powers, S., Qian, J., Jung, K., Schuler, A., Shah, N. H., Hastie, T., and Tibshirani, R · 2018
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A comparison of methods for model selection when estimating individual treatment effects
Schuler, A., Baiocchi, M., Tibshirani, R., and Shah, N · 2018
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Arjovsky, M., Bottou, L., Gulrajani, I., and Lopez-Paz, D · 2019
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Optimal doubly robust estimation of heterogeneous causal effects
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Realcause: Realistic causal inference benchmarking
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Beyond accuracy: Behavioral testing of nlp models with checklist
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Sense and sensitivity analysis: Simple post-hoc analysis of bias due to unobserved confounding
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Differentiable causal discovery under unmeasured confounding
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