Sensitivity Analysis for Causal Inference under Unmeasured Confounding and Measurement Error Problems
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Iván Díaz
und
Mark J. van der Laan
Abstract
In this article, we present a sensitivity analysis for drawing inferences about parameters that are not estimable from observed data without additional assumptions. We present the methodology using two different examples: a causal parameter that is not identifiable due to violations of the randomization assumption, and a parameter that is not estimable in the nonparametric model due to measurement error. Existing methods for tackling these problems assume a parametric model for the type of violation to the identifiability assumption and require the development of new estimators and inference for every new model. The method we present can be used in conjunction with any existing asymptotically linear estimator of an observed data parameter that approximates the unidentifiable full data parameter and does not require the study of additional models.
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©2013 by Walter de Gruyter Berlin / Boston
Artikel in diesem Heft
- Masthead
- Masthead
- Research Articles
- Sensitivity Analysis for Causal Inference under Unmeasured Confounding and Measurement Error Problems
- Assessing the Causal Effect of Policies: An Example Using Stochastic Interventions
- Novel Point Estimation from a Semiparametric Ratio Estimator (SPRE): Long-Term Health Outcomes from Short-Term Linear Data, with Application to Weight Loss in Obesity
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- Semiparametric Regression Analysis of Clustered Interval-Censored Failure Time Data with Informative Cluster Size
- A Weighting Analogue to Pair Matching in Propensity Score Analysis
- Alternative Monotonicity Assumptions for Improving Bounds on Natural Direct Effects
- Estimation of Risk Ratios in Cohort Studies with a Common Outcome: A Simple and Efficient Two-stage Approach
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- Commentary
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Artikel in diesem Heft
- Masthead
- Masthead
- Research Articles
- Sensitivity Analysis for Causal Inference under Unmeasured Confounding and Measurement Error Problems
- Assessing the Causal Effect of Policies: An Example Using Stochastic Interventions
- Novel Point Estimation from a Semiparametric Ratio Estimator (SPRE): Long-Term Health Outcomes from Short-Term Linear Data, with Application to Weight Loss in Obesity
- Exact Nonparametric Confidence Bands for the Survivor Function
- Semiparametric Regression Analysis of Clustered Interval-Censored Failure Time Data with Informative Cluster Size
- A Weighting Analogue to Pair Matching in Propensity Score Analysis
- Alternative Monotonicity Assumptions for Improving Bounds on Natural Direct Effects
- Estimation of Risk Ratios in Cohort Studies with a Common Outcome: A Simple and Efficient Two-stage Approach
- Distance-Based Mapping of Disease Risk
- The Balanced Survivor Average Causal Effect
- Commentary
- Principal Stratification: A Broader Vision
