
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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Estimating Potential Outcome Distributions with Collaborating Causal Networks
Many causal inference approaches have focused on identifying an individu...
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The role of exchangeability in causal inference
The notion of exchangeability has been recognized in the causal inferenc...
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Flexible sensitivity analysis for observational studies without observable implications
A fundamental challenge in observational causal inference is that assump...
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On Geometry of Information Flow for Causal Inference
Causal inference is perhaps one of the most fundamental concepts in scie...
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Causal Inference in CaseControl Studies
We investigate identification of causal parameters in casecontrol and r...
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Who Make Drivers Stop? Towards Drivercentric Risk Assessment: Risk Object Identification via Causal Inference
We propose a framework based on causal inference for risk object identif...
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Quantifying Sufficient Randomness for Causal Inference
Spurious association arises from covariance between propensity for the treatment and individual risk for the outcome. For sensitivity analysis with stochastic counterfactuals we introduce a methodology to characterize uncertainty in causal inference from natural experiments and quasiexperiments. Our sensitivity parameters are standardized measures of variation in propensity and individual risk, and one minus their geometric mean is an intuitive measure of randomness in the data generating process. Within our latent propensityrisk model, we show how to compute from contingency table data a threshold, T, of sufficient randomness for causal inference. If the actual randomness of the data generating process exceeds this threshold then causal inference is warranted.
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