Fetching the paper…
Reading the bibliography…
We consider estimation of a total causal effect from observational data via covariate adjustment.
A new look at the statistical model identification
Hirotugu Akaike · 1974
Earlier work this paper cites.
Applied regression analysis and other multivariable methods
David G. Kleinbaum and Lawrence L. Kupper · 1978
Earlier work this paper cites.
Estimating the dimension of a model
Gideon Schwarz · 1978
Earlier work this paper cites.
A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect
James Robins · 1986
Earlier work this paper cites.
Equivalence and synthesis of causal models
TS Verma and Judea Pearl · 1990
Earlier work this paper cites.
Identifiability and exchangeability for direct and indirect effects
James M. Robins and Sander Greenland · 1992
Earlier work this paper cites.
Causal inference and causal explanation with background knowledge
Christopher Meek · 1995
Earlier work this paper cites.
Regression shrinkage and selection via the lasso
Robert Tibshirani · 1996
Earlier work this paper cites.
A characterization of Markov equivalence classes for acyclic digraphs
Steen A. Andersson, David Madigan, and Michael D. Perlman · 1997
Earlier work this paper cites.
On the role of the propensity score in efficient semiparametric estimation of average treatment effects
Jinyong Hahn · 1998
Earlier work this paper cites.
Causation, prediction, and search
Peter Spirtes, Clark Glymour, and Richard Scheines · 2000
Earlier work this paper cites.
Direct and indirect effects
Judea Pearl · 2001
Earlier work this paper cites.
Optimal structure identification with greedy search
David Maxwell Chickering · 2002
Earlier work this paper cites.
Efficient estimation of average treatment effects using the estimated propensity score
Keisuke Hirano, Guido W. Imbens, and Geert Ridder · 2003
Earlier work this paper cites.
Covariate selection for estimating the causal effect of control plans by using causal diagrams
Manabu Kuroki and Masami Miyakawa · 2003
Earlier work this paper cites.
Markov properties for acyclic directed mixed graphs
Thomas Richardson · 2003
Earlier work this paper cites.
On the identification of causal effects
Jin Tian and Judea Pearl · 2003
Earlier work this paper cites.
Selection of identifiability criteria for total effects by using path diagrams
Manabu Kuroki and Zhihong Cai · 2004
Earlier work this paper cites.
Stratification and weighting via the propensity score in estimation of causal treatment effects: A comparative study
Jared K. Lunceford and Marie Davidian · 2004
Earlier work this paper cites.
Model-free variable selection
Lexin Li, R. Dennis Cook, and Christopher J. Nachtsheim · 2005
Earlier work this paper cites.
Regularization and variable selection via the elastic net
Hui Zou and Trevor Hastie · 2005
Earlier work this paper cites.
Variable selection for propensity score models
M. Alan Brookhart, Sebastian Schneeweiss, Kenneth J. Rothman, Robert J. Glynn, Jerry Avorn, and Til Stürmer · 2006
Earlier work this paper cites.
A linear non-Gaussian acyclic model for causal discovery
Shohei Shimizu, Patrik O. Hoyer, Aapo Hyvärinen, and Antti Kerminen · 2006
Earlier work this paper cites.
The Schizotypic Syndrome Questionnaire (SSQ): Psychometrics, validation and norms
Dirk van Kampen · 2006
Earlier work this paper cites.
On model selection consistency of Lasso
Peng Zhao and Bin Yu · 2006
Cited alongside, same era.
Can one estimate the unconditional distribution of post-model-selection estimators?
Hannes Leeb and Benedikt M. Pötscher · 2008
Cited alongside, same era.
Sup-norm convergence rate and sign concentration property of Lasso and Dantzig estimators
Karim Lounici · 2008
Cited alongside, same era.
Estimating high-dimensional intervention effects from observational data
Marloes H. Maathuis, Markus Kalisch, and Peter Bühlmann · 2009
Cited alongside, same era.
Causality: Models, reasoning, and inference
Judea Pearl · 2009
Cited alongside, same era.
Identifying the consequences of dynamic treatment strategies: A decision-theoretic overview
A. Philip Dawid and Vanessa Didelez · 2010
Cited alongside, same era.
Statistical foundations for model-based adjustments
Sander Greenland and Neil Pearce · 2015
Later among the works it cites.
A generalised back-door criterion
Marloes H. Maathuis and Diego Colombo · 2015
Later among the works it cites.
A complete generalized adjustment criterion
Emilija Perković, Johannes Textor, Markus Kalisch, and Marloes H. Maathuis · 2015
Later among the works it cites.
Exact post-selection inference, with application to the lasso
Jason D. Lee, Dennis L. Sun, Yuekai Sun, and Jonathan E. Taylor · 2016
Later among the works it cites.
Estimating the effect of joint interventions from observational data in sparse high-dimensional settings
Preetam Nandy, Marloes H. Maathuis, and Thomas S. Richardson · 2017
Later among the works it cites.
Interpreting and using CPDAGs with background knowledge
Emilija Perković, Markus Kalisch, and Maloes H. Maathuis · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Regression modeling strategies
Frank E. Harrell, Jr · 2010
Cited alongside, same era.
DAG program: Identifying minimal sufficient adjustment sets
Sven Knüppel and Andreas Stang · 2010
Cited alongside, same era.
Predicting causal effects in large-scale systems from observational data
Marloes H. Maathuis, Diego Colombo, Markus Kalisch, and Peter Bühlmann · 2010
Cited alongside, same era.
On the validity of covariate adjustment for estimating causal effects
Ilya Shpitser, Tyler VanderWeele, and James M. Robins · 2010
Cited alongside, same era.
Covariate selection for the nonparametric estimation of an average treatment effect
Xavier de Luna, Ingeborg Waernbaum, and Thomas S Richardson · 2011
Cited alongside, same era.
Adjustment criteria in causal diagrams: An algorithmic perspective
Johannes Textor and Maciej Liśkiewicz · 2011
Cited alongside, same era.
Later among the works it cites.
Nested Markov properties for acyclic directed mixed graphs
Thomas S. Richardson, Robin J. Evans, James M. Robins, and Ilya Shpitser · 2017
Later among the works it cites.
Outcome-adaptive lasso: Variable selection for causal inference
Susan M. Shortreed and Ashkan Ertefaie · 2017
Later among the works it cites.
Double/debiased machine learning for treatment and structural parameters
Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, and James Robins · 2018
Later among the works it cites.
Model selection and regression t-statistics
DeWayne Derryberry, Ken Aho, John Edwards, and Teri Peterson · 2018
Later among the works it cites.
A novel approach to identify the miRNA-mRNA causal regulatory modules in cancer
Jiawei Luo, Wei Huang, and Buwen Cao · 2018
Later among the works it cites.
Complete graphical characterization and construction of adjustment sets in Markov equivalence classes of ancestral graphs
Emilija Perković, Johannes Textor, Markus Kalisch, and Marloes H. Maathuis · 2018
Later among the works it cites.
Graphical criteria for efficient total effect estimation via adjustment in causal linear models
Leonard Henckel, Emilija Perković, and Marloes H. Maathuis · 2019
Later among the works it cites.
pcalg: Methods for Graphical Models and Causal Inference , 2019
Markus Kalisch, Alain Hauser, Martin Maechler, Diego Colombo, Doris Entner, Patrik Hoyer, Antti Hyttinen, Jonas Peters, Nicoletta Andri, Emilija Perkovic, Preetam Nandy, Philipp Ruetimann, Daniel Stekhoven, Manuel Schuerch, and Marco Eigenmann · 2019
Later among the works it cites.
R: A Language and Environment for Statistical Computing
R Core Team · 2019
Later among the works it cites.
Bootstrapping and sample splitting for high-dimensional, assumption-lean inference
Alessandro Rinaldo, Larry Wasserman, and Max G’Sell · 2019
Later among the works it cites.
A unifying approach for doubly-robust l 1 l_{1} regularized estimation of causal contrasts
Ezequiel Smucler, Andrea Rotnitzky, and James M. Robins · 2019
Later among the works it cites.
Covariate selection strategies for causal inference: Classification and comparison
Janine Witte and Vanessa Didelez · 2019
Later among the works it cites.
Efficient least squares for estimating total effects under linearity and causal sufficiency
F. Richard Guo and Emilija Perković · 2020
Closest in time.
Identifying causal effects in maximally oriented partially directed acyclic graphs
Emilija Perković · 2020
Closest in time.
Efficient adjustment sets for population average treatment effect estimation in non-parametric causal graphical models
Andrea Rotnitzky and Ezequiel Smucler · 2020
Closest in time.
The hardness of conditional independence testing and the generalised covariance measure
Rajen D. Shah and Jonas Peters · 2020
Closest in time.
Efficient adjustment sets in causal graphical models with hidden variables
Ezequiel Smucler, Facundo Sapienza, and Andrea Rotnitzky · 2020
Closest in time.