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07/17/2023
Results on Counterfactual Invariance
In this paper we provide a theoretical analysis of counterfactual invari...
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06/26/2023
PWSHAP: A Path-Wise Explanation Model for Targeted Variables
Predictive black-box models can exhibit high accuracy but their opaque n...
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04/05/2023
Many Data: Combine Experimental and Observational Data through a Power Likelihood
Randomized controlled trials are commonly regarded as the gold standard ...
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12/09/2022
Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects Even Under One Sided Overlap
As causal inference becomes more widespread the importance of having goo...
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09/14/2022
Latent-free equivalent mDAGs
We show that the marginal model for a discrete directed acyclic graph (D...
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05/26/2022
Regression Identifiability and Edge Interventions in Linear Structural Equation Models
In this paper, we introduce a new identifiability criteria for linear st...
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11/25/2021
A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment
Causal inference grows increasingly complex as the number of confounders...
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09/08/2021
Parameterizing and Simulating from Causal Models
Many statistical problems in causal inference involve a probability dist...
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06/14/2021
Dependency in DAG models with Hidden Variables
Directed acyclic graph models with hidden variables have been much studi...
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11/13/2018
Towards Characterising Bayesian Network Models under Selection
Real-life statistical samples are often plagued by selection bias, which...
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09/18/2018
Low-Voltage Distribution Network Impedances Identification Based on Smart Meter Data
Under conditions of high penetration of renewables, the low-voltage (LV)...
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01/25/2018
Model selection and local geometry
We consider problems in model selection caused by the geometry of models...
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08/07/2015
Distributional Equivalence and Structure Learning for Bow-free Acyclic Path Diagrams
We consider the problem of structure learning for bow-free acyclic path ...
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01/09/2015
Margins of discrete Bayesian networks
Bayesian network models with latent variables are widely used in statist...
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09/26/2013
Sparse Nested Markov models with Log-linear Parameters
Hidden variables are ubiquitous in practical data analysis, and therefor...
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03/15/2012
