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05/16/2023
The Hardness of Reasoning about Probabilities and Causality
We study formal languages which are capable of fully expressing quantita...
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02/28/2023
Practical Algorithms for Orientations of Partially Directed Graphical Models
In observational studies, the true causal model is typically unknown and...
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01/28/2023
Efficient Enumeration of Markov Equivalent DAGs
Enumerating the directed acyclic graphs (DAGs) of a Markov equivalence c...
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11/29/2022
Finding Front-Door Adjustment Sets in Linear Time
Front-door adjustment is a classic technique to estimate causal effects ...
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05/05/2022
Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applications
Counting and sampling directed acyclic graphs from a Markov equivalence ...
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03/03/2022
Identification in Tree-shaped Linear Structural Causal Models
Linear structural equation models represent direct causal effects as dir...
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12/17/2020
Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs
Counting and uniform sampling of directed acyclic graphs (DAGs) from a M...
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10/06/2020
Recovering Causal Structures from Low-Order Conditional Independencies
One of the common obstacles for learning causal models from data is that...
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02/28/2018
Separators and Adjustment Sets in Causal Graphs: Complete Criteria and an Algorithmic Framework
Principled reasoning about the identifiability of causal effects from no...
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01/24/2018
On the Gold Standard for Security of Universal Steganography
While symmetric-key steganography is quite well understood both in the i...
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08/02/2015
Learning from Pairwise Marginal Independencies
We consider graphs that represent pairwise marginal independencies among...
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02/14/2012