
Postselection Problems for Causal Inference with Invalid Instruments: A Solution Using Searching and Sampling
Instrumental variable method is among the most commonly used causal infe...
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Ivy: Instrumental Variable Synthesis for Causal Inference
A popular way to estimate the causal effect of a variable x on y from ob...
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Valid Causal Inference with (Some) Invalid Instruments
Instrumental variable methods provide a powerful approach to estimating ...
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Bayesian causal inference with some invalid instrumental variables
In observational studies, instrumental variables estimation is greatly u...
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Outcome model free causal inference with ultrahigh dimensional covariates
Causal inference has been increasingly reliant on observational studies ...
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Causal Inference with the Instrumental Variable Approach and Bayesian Nonparametric Machine Learning
We provide a new flexible framework for inference with the instrumental ...
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DAG With Omitted Objects Displayed (DAGWOOD): A framework for revealing causal assumptions in DAGs
Directed acyclic graphs (DAGs) are frequently used in epidemiology as a ...
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MASSIVE: Tractable and Robust Bayesian Learning of ManyDimensional Instrumental Variable Models
The recent availability of huge, manydimensional data sets, like those arising from genomewide association studies (GWAS), provides many opportunities for strengthening causal inference. One popular approach is to utilize these manydimensional measurements as instrumental variables (instruments) for improving the causal effect estimate between other pairs of variables. Unfortunately, searching for proper instruments in a manydimensional set of candidates is a daunting task due to the intractable model space and the fact that we cannot directly test which of these candidates are valid, so most existing search methods either rely on overly stringent modeling assumptions or fail to capture the inherent model uncertainty in the selection process. We show that, as long as at least some of the candidates are (close to) valid, without knowing a priori which ones, they collectively still pose enough restrictions on the target interaction to obtain a reliable causal effect estimate. We propose a general and efficient causal inference algorithm that accounts for model uncertainty by performing Bayesian model averaging over the most promising manydimensional instrumental variable models, while at the same time employing weaker assumptions regarding the data generating process. We showcase the efficiency, robustness and predictive performance of our algorithm through experimental results on both simulated and realworld data.
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