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Covariate adjustment is a widely used approach to estimate total causal effects from observational data.
A new approach to causal inference in mortality studies with a sustained exposure period-application to control of the healthy worker survivor effect
Robins, J. (1986) · 1986
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Comment: Graphical models, causality and intervention
Pearl, J. (1993) · 1993
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Causal inference and causal explanation with background knowledge
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Identification of causal effects using instrumental variables
Angrist, J. D., Imbens, G. W., and Rubin, D. B. (1996) · 1996
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Causation, Prediction, and Search
Spirtes, P., Glymour, C., and Scheines, R. (2000) · 2000
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Ancestral graph Markov models
Richardson, T. and Spirtes, P. (2002) · 2002
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A general identification condition for causal effects
Tian, J. and Pearl, J. (2002) · 2002
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Optimal structure identification with greedy search
Chickering, D. M. (2003) · 2003
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Identification of joint interventional distributions in recursive semi-markovian causal models
Shpitser, I. and Pearl, J. (2006) · 2006
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Causal Inference and Reasoning in Causally Insufficient Systems
Zhang, J. (2006) · 2006
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Author’s reply
Rubin, D. (2008) · 2008
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Letter to the editor
Shrier, I. (2008) · 2008
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Reducing bias through directed acyclic graphs
Shrier, I. and Platt, R. W. (2008) · 2008
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Zhang, J. (2008) · 2008
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Markov equivalence for ancestral graphs
Ali, R. A., Richardson, T. S., and Spirtes, P. (2009) · 2009
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Estimating high-dimensional intervention effects from observational data
Maathuis, M. H., Kalisch, M., and Bühlmann, P. (2009) · 2009
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Learning high-dimensional directed acyclic graphs with latent and selection variables
Colombo, D., Maathuis, M. H., Kalisch, M., and Richardson, T. S. (2012) · 2012
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Appendum to “On the validity of covariate adjustment for estimating causal effects”
Shpitser, I. (2012) · 2012
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Learning sparse causal models is not NP-hard
Claassen, T., Mooij, J., and Heskes, T. (2013) · 2013
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The table 2 fallacy: presenting and interpreting confounder and modifier coefficients
Westreich, D. and Greenland, S. (2013) · 2013
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Recovering from selection bias in causal and statistical inference
Bareinboim, E., Tian, J., and Pearl, J. (2014) · 2014
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Order-independent constraint-based causal structure learning
Colombo, D. and Maathuis, M. H. (2014) · 2014
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Pearl, J. (2009) · 2009
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Predicting causal effects in large-scale systems from observational data
Maathuis, M. H., Colombo, D., Kalisch, M., and Bühlmann, P. (2010) · 2010
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On the validity of covariate adjustment for estimating causal effects
Shpitser, I., VanderWeele, T., and Robins, J. M. (2010) · 2010
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Adjustment criteria in causal diagrams: An algorithmic perspective
Textor, J. and Liśkiewicz, M. (2011) · 2011
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Constructing separators and adjustment sets in ancestral graphs
van der Zander, B., Liśkiewicz, M., and Textor, J. (2014) · 2014
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Restoring causal analysis to structural equation modeling
West, S. G. and Koch, T. (2014) · 2014
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A generalized back-door criterion
Maathuis, M. H. and Colombo, D. (2015) · 2015
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Structural intervention distance (SID) for evaluating causal graphs
Peters, J. and Bühlmann, P. (2015) · 2015
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