
Computational Causal Inference
We introduce computational causal inference as an interdisciplinary fiel...
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RealCause: Realistic Causal Inference Benchmarking
There are many different causal effect estimators in causal inference. H...
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Atlantic Causal Inference Conference (ACIC) Data Analysis Challenge 2017
This brief note documents the data generating processes used in the 2017...
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Causal Inference in CaseControl Studies
We investigate identification of causal parameters in casecontrol and r...
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Slamming the sham: A Bayesian model for adaptive adjustment with noisy control data
It is not always clear how to adjust for control data in causal inferenc...
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Bivariate temporal orders for causal inference
Causality analysis may be carried out at different levels of detail, e.g...
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What can be estimated? Identifiability, estimability, causal inference and illposed inverse problems
Here we consider, in the context of causal inference, the general questi...
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Comment on "Statistical Modeling: The Two Cultures" by Leo Breiman
Motivated by Breiman's rousing 2001 paper on the "two cultures" in statistics, we consider the role that different modeling approaches play in causal inference. We discuss the relationship between model complexity and causal (mis)interpretation, the relative merits of plugin versus targeted estimation, issues that arise in tuning flexible estimators of causal effects, and some outstanding cultural divisions in causal inference.
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