
Causal programming: inference with structural causal models as finding instances of a relation
This paper proposes a causal inference relation and causal programming a...
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Entropic Causal Inference: Identifiability and Finite Sample Results
Entropic causal inference is a framework for inferring the causal direct...
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A Practical Introduction to Bayesian Estimation of Causal Effects: Parametric and Nonparametric Approaches
Substantial advances in Bayesian methods for causal inference have been ...
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Optimized Partial Identification Bounds for Regression Discontinuity Designs with Manipulation
The regression discontinuity (RD) design is one of the most popular quas...
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Causal inference using Bayesian nonparametric quasiexperimental design
The de facto standard for causal inference is the randomized controlled ...
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On finitepopulation Bayesian inferences for 2^K factorial designs with binary outcomes
Inspired by the pioneering work of Rubin (1978), we employ the potential...
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A Bayesian Approach to Constraint Based Causal Inference
We target the problem of accuracy and robustness in causal inference fro...
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A SimulationBased Test of Identifiability for Bayesian Causal Inference
This paper introduces a procedure for testing the identifiability of Bayesian models for causal inference. Although the docalculus is sound and complete given a causal graph, many practical assumptions cannot be expressed in terms of graph structure alone, such as the assumptions required by instrumental variable designs, regression discontinuity designs, and withinsubjects designs. We present simulationbased identifiability (SBI), a fully automated identification test based on a particle optimization scheme with simulated observations. This approach expresses causal assumptions as priors over functions in a structural causal model, including flexible priors using Gaussian processes. We prove that SBI is asymptotically sound and complete, and produces practical finitesample bounds. We also show empirically that SBI agrees with known results in graphbased identification as well as with widelyheld intuitions for designs in which graphbased methods are inconclusive.
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