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The estimation of treatment effects is a pervasive problem in medicine.
Multiple causes: A causal graphical view
Wang, Y. and Blei, D. M · 1905
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Multiple causes: A causal graphical view
Wang, Y. and Blei, D. M · 1905
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Sur les applications de la théorie des probabilités aux experiences agricoles: Essai des principes
Neyman, J · 1923
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Sur les applications de la théorie des probabilités aux experiences agricoles: Essai des principes
Neyman, J · 1923
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Bayesian inference for causal effects: The role of randomization
Rubin, D. B · 1978
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Bayesian inference for causal effects: The role of randomization
Rubin, D. B · 1978
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Bayesianly justifiable and relevant frequency calculations for the applies statistician
Rubin, D. B · 1984
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Bayesianly justifiable and relevant frequency calculations for the applies statistician
Rubin, D. B · 1984
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Correcting for non-compliance in randomized trials using structural nested mean models
Robins, J. M · 1994
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Correcting for non-compliance in randomized trials using structural nested mean models
Robins, J. M · 1994
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Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
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Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
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Probabilistic principal component analysis
Tipping, M. E. and Bishop, C. M · 1999
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Probabilistic principal component analysis
Tipping, M. E. and Bishop, C. M · 1999
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The generalized dynamic-factor model: Identification and estimation
Forni, M., Hallin, M., Lippi, M., and Reichlin, L · 2000
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Causation, prediction, and search
Spirtes, P., Glymour, C. N., Scheines, R., and Heckerman, D · 2000
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The generalized dynamic-factor model: Identification and estimation
Forni, M., Hallin, M., Lippi, M., and Reichlin, L · 2000
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Causation, prediction, and search
Spirtes, P., Glymour, C. N., Scheines, R., and Heckerman, D · 2000
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Marginal structural models to estimate the joint causal effect of nonrandomized treatments
Hernán, M. A., Brumback, B., and Robins, J. M · 2001
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Marginal structural models to estimate the joint causal effect of nonrandomized treatments
Hernán, M. A., Brumback, B., and Robins, J. M · 2001
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Estimating the effects of continuous-valued interventions using generative adversarial networks
Bica, I., Jordon, J., and van der Schaar, M · 2002
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Estimating the effects of continuous-valued interventions using generative adversarial networks
Bica, I., Jordon, J., and van der Schaar, M · 2002
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Causal inference with general treatment regimes: Generalizing the propensity score
Imai, K. and Van Dyk, D. A · 2004
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Causal inference with general treatment regimes: Generalizing the propensity score
Imai, K. and Van Dyk, D. A · 2004
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Measuring the effects of monetary policy: a factor-augmented vector autoregressive (favar) approach
Bernanke, B. S., Boivin, J., and Eliasz, P · 2005
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The generalized dynamic factor model: one-sided estimation and forecasting
Forni, M., Hallin, M., Lippi, M., and Reichlin, L · 2005
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Measuring the effects of monetary policy: a factor-augmented vector autoregressive (favar) approach
Bernanke, B. S., Boivin, J., and Eliasz, P · 2005
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The generalized dynamic factor model: one-sided estimation and forecasting
Forni, M., Hallin, M., Lippi, M., and Reichlin, L · 2005
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Foundations of modern probability
Kallenberg, O · 2006
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Foundations of modern probability
Kallenberg, O · 2006
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Genetic risk profiles for cancer susceptibility and therapy response
Bartsch, H., Dally, H., Popanda, O., Risch, A., and Schmezer, P · 2007
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Genetic risk profiles for cancer susceptibility and therapy response
Bartsch, H., Dally, H., Popanda, O., Risch, A., and Schmezer, P · 2007
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Causal graphical models with latent variables: learning and inference
Leray, P., Meganek, S., Maes, S., and Manderick, B · 2008
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Statistical modeling of causal effects in continuous time
Lok, J. J. et al · 2008
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Estimation of the causal effects of time-varying exposures
Robins, J. M. and Hernán, M. A · 2008
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Causal graphical models with latent variables: learning and inference
Leray, P., Meganek, S., Maes, S., and Manderick, B · 2008
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Statistical modeling of causal effects in continuous time
Lok, J. J. et al · 2008
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Estimation of the causal effects of time-varying exposures
Robins, J. M. and Hernán, M. A · 2008
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Causality
Pearl, J · 2009
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Time-modified confounding
Platt, R. W., Schisterman, E. F., and Cole, S. R · 2009
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Causality
Pearl, J · 2009
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Time-modified confounding
Platt, R. W., Schisterman, E. F., and Cole, S. R · 2009
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Bayesian nonparametric modeling for causal inference
Hill, J. L · 2011
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Bayesian nonparametric modeling for causal inference
Hill, J. L · 2011
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Estimating the effects of multiple time-varying exposures using joint marginal structural models: alcohol consumption, injection drug use, and hiv acquisition
New drugs, new toxicities: severe side effects of modern targeted and immunotherapy of cancer and their management
Kroschinsky, F., Stölzel, F., von Bonin, S., Beutel, G., Kochanek, M., Kiehl, M., and Schellongowski, P · 2017
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Causal effect inference with deep latent-variable models
Louizos, C., Shalit, U., Mooij, J. M., Sontag, D., Zemel, R., and Welling, M · 2017
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Handling time varying confounding in observational research
Mansournia, M. A., Etminan, M., Danaei, G., Kaufman, J. S., and Collins, G · 2017
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Reliable decision support using counterfactual models
Schulam, P. and Saria, S · 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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Howe, C. J., Cole, S. R., Mehta, S. H., and Kirk, G. D · 2012
Cited alongside, same era.
Estimating the effects of multiple time-varying exposures using joint marginal structural models: alcohol consumption, injection drug use, and hiv acquisition
Howe, C. J., Cole, S. R., Mehta, S. H., and Kirk, G. D · 2012
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Good practices for quantitative bias analysis
Lash, T. L., Fox, M. P., MacLehose, R. F., Maldonado, G., McCandless, L. C., and Greenland, S · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Good practices for quantitative bias analysis
Lash, T. L., Fox, M. P., MacLehose, R. F., Maldonado, G., McCandless, L. C., and Greenland, S · 2014
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Soleimani, H., Subbaswamy, A., and Saria, S · 2017
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Estimation and inference of heterogeneous treatment effects using random forests
Wager, S. and Athey, S · 2017
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Limits of estimating heterogeneous treatment effects: Guidelines for practical algorithm design
Alaa, A. and Schaar, M · 2018
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Accounting for hidden common causes when infering cause and effect from observational data
Heckerman, D · 2018
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Estimation of individual treatment effect in latent confounder models via adversarial learning
Lee, C., Mastronarde, N., and van der Schaar, M · 2018
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Forecasting treatment responses over time using recurrent marginal structural networks
Lim, B., Alaa, A., and van der Schaar, M · 2018
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Identifying causal effects with proxy variables of an unmeasured confounder
Miao, W., Geng, Z., and Tchetgen Tchetgen, E. J · 2018
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Comparison of strategies for scalable causal discovery of latent variable models from mixed data
Raghu, V. K., Ramsey, J. D., Morris, A., Manatakis, D. V., Sprites, P., Chrysanthis, P. K., Glymour, C., and Benos, P. V · 2018
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Multiple causal inference with latent confounding
Ranganath, R. and Perotte, A · 2018
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Global sensitivity analysis for repeated measures studies with informative drop-out: A semi-parametric approach
Scharfstein, D., McDermott, A., Díaz, I., Carone, M., Lunardon, N., and Turkoz, I · 2018
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Implicit causal models for genome-wide association studies
Tran, D. and Blei, D. M · 2018
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Treatment resistance in urothelial carcinoma: an evolutionary perspective
Vlachostergios, P. J. and Faltas, B. M · 2018
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Ganite: Estimation of individualized treatment effects using generative adversarial nets
Yoon, J., Jordon, J., and van der Schaar, M · 2018
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Limits of estimating heterogeneous treatment effects: Guidelines for practical algorithm design
Alaa, A. and Schaar, M · 2018
Later among the works it cites.
Accounting for hidden common causes when infering cause and effect from observational data
Heckerman, D · 2018
Later among the works it cites.
Estimation of individual treatment effect in latent confounder models via adversarial learning
Lee, C., Mastronarde, N., and van der Schaar, M · 2018
Later among the works it cites.
Forecasting treatment responses over time using recurrent marginal structural networks
Lim, B., Alaa, A., and van der Schaar, M · 2018
Later among the works it cites.
Identifying causal effects with proxy variables of an unmeasured confounder
Miao, W., Geng, Z., and Tchetgen Tchetgen, E. J · 2018
Later among the works it cites.
Comparison of strategies for scalable causal discovery of latent variable models from mixed data
Raghu, V. K., Ramsey, J. D., Morris, A., Manatakis, D. V., Sprites, P., Chrysanthis, P. K., Glymour, C., and Benos, P. V · 2018
Later among the works it cites.
Multiple causal inference with latent confounding
Ranganath, R. and Perotte, A · 2018
Later among the works it cites.
Global sensitivity analysis for repeated measures studies with informative drop-out: A semi-parametric approach
Scharfstein, D., McDermott, A., Díaz, I., Carone, M., Lunardon, N., and Turkoz, I · 2018
Later among the works it cites.
Implicit causal models for genome-wide association studies
Tran, D. and Blei, D. M · 2018
Later among the works it cites.
Treatment resistance in urothelial carcinoma: an evolutionary perspective
Vlachostergios, P. J. and Faltas, B. M · 2018
Later among the works it cites.
Ganite: Estimation of individualized treatment effects using generative adversarial nets
Yoon, J., Jordon, J., and van der Schaar, M · 2018
Later among the works it cites.
On multi-cause approaches to causal inference with unobserved counfounding: Two cautionary failure cases and a promising alternative
D’Amour, A · 2019
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Multi-cause causal inference with unmeasured confounding and binary outcome
Kong, D., Yang, S., and Wang, L · 2019
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Current use of vasopressors in septic shock
Scheeren, T. W., Bakker, J., De Backer, D., Annane, D., Asfar, P., Boerma, E. C., Cecconi, M., Dubin, A., Dünser, M. W., Duranteau, J., et al · 2019
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On multi-cause approaches to causal inference with unobserved counfounding: Two cautionary failure cases and a promising alternative
D’Amour, A · 2019
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Multi-cause causal inference with unmeasured confounding and binary outcome
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Current use of vasopressors in septic shock
Scheeren, T. W., Bakker, J., De Backer, D., Annane, D., Asfar, P., Boerma, E. C., Cecconi, M., Dubin, A., Dünser, M. W., Duranteau, J., et al · 2019
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Learning overlapping representations for the estimation of individualized treatment effects
Zhang, Y., Bellot, A., and van der Schaar, M · 2020
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Learning overlapping representations for the estimation of individualized treatment effects
Zhang, Y., Bellot, A., and van der Schaar, M · 2020
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