
Multiply Robust Causal Mediation Analysis with Continuous Treatments
In many applications, researchers are interested in the direct and indir...
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MarkovRestricted Analysis of Randomized Trials with NonMonotone Missing Binary Outcomes: Sensitivity Analysis and Identification Results
Scharfstein et al. (2021) developed a sensitivity analysis model for ana...
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Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals
A moment function is called doubly robust if it is comprised of two nuis...
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Generating Synthetic Text Data to Evaluate Causal Inference Methods
Drawing causal conclusions from observational data requires making assum...
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Partial Identifiability in Discrete Data With Measurement Error
When data contains measurement errors, it is necessary to make assumptio...
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Differentiable Causal Discovery Under Unmeasured Confounding
The data drawn from biological, economic, and social systems are often c...
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Path Dependent Structural Equation Models
Causal analyses of longitudinal data generally assume structure that is ...
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An Interventionist Approach to Mediation Analysis
Judea Pearl's insight that, when errors are assumed independent, the Pur...
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Multivariate Counterfactual Systems And Causal Graphical Models
Among Judea Pearl's many contributions to Causality and Statistics, the ...
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Deriving Bounds and Inequality Constraints Using LogicalRelations Among Counterfactuals
Causal parameters may not be point identified in the presence of unobser...
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A Semiparametric Approach to Interpretable Machine Learning
Black box models in machine learning have demonstrated excellent predict...
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Explaining The Behavior Of BlackBox Prediction Algorithms With Causal Learning
We propose to explain the behavior of blackbox prediction methods (e.g....
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Nonmyopic and pseudononmyopic approaches to optimal sequential design in the presence of covariates
In sequential experiments, subjects become available for the study over ...
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Full Law Identification In Graphical Models Of Missing Data: Completeness Results
Missing data has the potential to affect analyses conducted in all field...
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General Identification of Dynamic Treatment Regimes Under Interference
In many applied fields, researchers are often interested in tailoring tr...
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Identification Methods With Arbitrary Interventional Distributions as Inputs
Causal inference quantifies causeeffect relationships by estimating cou...
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Semiparametric Inference For Causal Effects In Graphical Models With Hidden Variables
The last decade witnessed the development of algorithms that completely ...
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Counterexamples to "The Blessings of Multiple Causes" by Wang and Blei
This brief note is meant to complement our previous comment on "The Bles...
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Comment on "Blessings of Multiple Causes"
The premise of the deconfounder method proposed in "Blessings of Multipl...
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Optimal Training of Fair Predictive Models
Recently there has been sustained interest in modifying prediction algor...
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Semiparametric Inference for Nonmonotone MissingNotatRandom Data: the No SelfCensoring Model
We study the identification and estimation of statistical functionals of...
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Identification In Missing Data Models Represented By Directed Acyclic Graphs
Missing data is a pervasive problem in data analyses, resulting in datas...
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Causal Inference Under Interference And Network Uncertainty
Classical causal and statistical inference methods typically assume the ...
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Conditionallyadditivenoise Models for Structure Learning
Constraintbased structure learning algorithms infer the causal structur...
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A Potential Outcomes Calculus for Identifying Conditional PathSpecific Effects
The docalculus is a wellknown deductive system for deriving connection...
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Identification and Estimation of Causal Effects from Dependent Data
The assumption that data samples are independent and identically distrib...
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Causal inference, social networks, and chain graphs
Traditionally, statistical and causal inference on human subjects relies...
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Challenges of Using Text Classifiers for Causal Inference
Causal understanding is essential for many kinds of decisionmaking, but...
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Estimation of Personalized Effects Associated With Causal Pathways
The goal of personalized decision making is to map a unit's characterist...
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Learning Optimal Fair Policies
We consider the problem of learning optimal policies from observational ...
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The generalized frontdoor criterion for estimation of indirect causal effects of a confounded treatment
The population intervention effect (PIE) of an exposure measures the exp...
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Fair Inference On Outcomes
In this paper, we consider the problem of fair statistical inference inv...
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Sparse Nested Markov models with Loglinear Parameters
Hidden variables are ubiquitous in practical data analysis, and therefor...
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On the definition of a confounder
The causal inference literature has provided a clear formal definition o...
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Parameter and Structure Learning in Nested Markov Models
The constraints arising from DAG models with latent variables can be nat...
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Effects of Treatment on the Treated: Identification and Generalization
Many applications of causal analysis call for assessing, retrospectively...
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On the Validity of Covariate Adjustment for Estimating Causal Effects
Identifying effects of actions (treatments) on outcome variables from ob...
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An Efficient Algorithm for Computing Interventional Distributions in Latent Variable Causal Models
Probabilistic inference in graphical models is the task of computing mar...
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Ilya Shpitser
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