scholarly journals Efficient Nonparametric Causal Inference with Missing Exposure Information

2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Edward H. Kennedy
Keyword(s):  
Causal Inference ◽  
Observational Studies ◽  
Missing At Random ◽  
Rates Of Convergence ◽  
Efficient Estimation ◽  
Nonparametric Model ◽  
Nonparametric Estimators ◽  
Asymptotically Normal ◽  
Flexible Machine ◽  
Nonparametric Efficiency

AbstractMissing exposure information is a very common feature of many observational studies. Here we study identifiability and efficient estimation of causal effects on vector outcomes, in such cases where treatment is unconfounded but partially missing. We consider a missing at random setting where missingness in treatment can depend not only on complex covariates, but also on post-treatment outcomes. We give a new identifying expression for average treatment effects in this setting, along with the efficient influence function for this parameter in a nonparametric model, which yields a nonparametric efficiency bound. We use this latter result to construct nonparametric estimators that are less sensitive to the curse of dimensionality than usual, e. g. by having faster rates of convergence than the complex nuisance estimators they rely on. Further we show that these estimators can be root-n consistent and asymptotically normal under weak nonparametric conditions, even when constructed using flexible machine learning. Finally we apply these results to the problem of causal inference with a partially missing instrumental variable.

scholarly journals SOCIAL AND BEHAVIORAL FACTORS IN COGNITIVE AGING: APPLYING THE CAUSAL INFERENCE FRAMEWORK IN OBSERVATIONAL STUDIES

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S323-S323
Author(s):  
Anja K Leist
Keyword(s):  
Machine Learning ◽  
Cognitive Decline ◽  
Causal Inference ◽  
Cognitive Aging ◽  
Behavioral Interventions ◽  
Observational Studies ◽  
Intervention Research ◽  
Learning Approaches ◽  
Behavioral Factors

Abstract Rationale: There is an urgent need to better understand how to maintain cognitive functioning at older ages with social and behavioral interventions, given that there is currently no medical cure available to prevent, halt or reverse the progression of cognitive decline and dementia. However, in current models, it is still not well established which factors (e.g. education, BMI, physical activity, sleep, depression) matter most at which ages, and which behavioral profiles are most protective against cognitive decline. In the last years, advances in the fields of causal inference and machine learning have equipped epidemiology and social sciences with methods and models to approach causal questions in observational studies. Method: The presentation will give an overview of the causal inference framework and different machine learning approaches to investigate cognitive aging. First, we will present relevant research questions on the role of social and behavioral factors in cognitive aging in observational studies. Second, we will introduce the causal inference framework and recent methods to visualize and compute the strength of causal paths. Third, promising machine learning approaches to arrive at robust predictions are presented. The 13-year follow-up from the European SHARE survey that employs well-established cognitive performance tests is used to demonstrate the usefulness of the approach. Discussion: The causal inference framework, combined with recent machine learning approaches and applied in observational studies, provides a robust alternative to intervention research. Advantages for investigations under the new framework, e.g., fewer ethical considerations compared to intervention research, as well as limitations are discussed.

scholarly journals Subjective Causality and Counterfactuals in the Social Sciences: Toward an Ethnographic Causality?

2019 ◽  
pp. 004912411985237
Author(s):  
Peter Abell ◽  
Ofer Engel
Keyword(s):  
Social Sciences ◽  
Causal Inference ◽  
Statistical Inference ◽  
Observational Studies ◽  
Causal Explanation ◽  
Statistical Concept ◽  
Ethnographic Studies ◽  
The Social ◽  
Subjective Evidence ◽  
Subsequent Comparison

The article explores the role that subjective evidence of causality and associated counterfactuals and counterpotentials might play in the social sciences where comparative cases are scarce. This scarcity rules out statistical inference based upon frequencies and usually invites in-depth ethnographic studies. Thus, if causality is to be preserved in such situations, a conception of ethnographic causal inference is required. Ethnographic causality inverts the standard statistical concept of causal explanation in observational studies, whereby comparison and generalization, across a sample of cases, are both necessary prerequisites for any causal inference. Ethnographic causality allows, in contrast, for causal explanation prior to any subsequent comparison or generalization.

Teaching Statistical Inference for Causal Effects in Experiments and Observational Studies

2004 ◽  
Vol 29 (3) ◽  
pp. 343-367 ◽  
Author(s):  
Donald B. Rubin
Keyword(s):  
Public Policy ◽  
Causal Inference ◽  
Statistical Inference ◽  
Undergraduate Students ◽  
Observational Studies ◽  
Causal Effects ◽  
Designed Experiments ◽  
Critical Activity ◽  
The One

Inference for causal effects is a critical activity in many branches of science and public policy. The field of statistics is the one field most suited to address such problems, whether from designed experiments or observational studies. Consequently, it is arguably essential that departments of statistics teach courses in causal inference to both graduate and undergraduate students. This article discusses an outline of such courses based on repeated experience over more than a decade.

scholarly journals Incremental intervention effects in studies with dropout and many timepoints#

2021 ◽  
Vol 9 (1) ◽  
pp. 302-344
Author(s):  
Kwangho Kim ◽  
Edward H. Kennedy ◽  
Ashley I. Naimi
Keyword(s):  
Sample Size ◽  
Propensity Scores ◽  
Infinite Time ◽  
Intervention Effects ◽  
Nonparametric Estimators ◽  
Dose Aspirin ◽  
Statistical Precision ◽  
Low Dose Aspirin ◽  
Nonparametric Efficiency ◽  
Subject Dropout

Abstract Modern longitudinal studies collect feature data at many timepoints, often of the same order of sample size. Such studies are typically affected by dropout and positivity violations. We tackle these problems by generalizing effects of recent incremental interventions (which shift propensity scores rather than set treatment values deterministically) to accommodate multiple outcomes and subject dropout. We give an identifying expression for incremental intervention effects when dropout is conditionally ignorable (without requiring treatment positivity) and derive the nonparametric efficiency bound for estimating such effects. Then we present efficient nonparametric estimators, showing that they converge at fast parametric rates and yield uniform inferential guarantees, even when nuisance functions are estimated flexibly at slower rates. We also study the variance ratio of incremental intervention effects relative to more conventional deterministic effects in a novel infinite time horizon setting, where the number of timepoints can grow with sample size and show that incremental intervention effects yield near-exponential gains in statistical precision in this setup. Finally, we conclude with simulations and apply our methods in a study of the effect of low-dose aspirin on pregnancy outcomes.

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