research
∙
05/16/2023
The Hardness of Reasoning about Probabilities and Causality
We study formal languages which are capable of fully expressing quantita...
share
research
∙
02/28/2023
Practical Algorithms for Orientations of Partially Directed Graphical Models
In observational studies, the true causal model is typically unknown and...
research
∙
01/28/2023
Efficient Enumeration of Markov Equivalent DAGs
Enumerating the directed acyclic graphs (DAGs) of a Markov equivalence c...
research
∙
11/29/2022
Finding Front-Door Adjustment Sets in Linear Time
Front-door adjustment is a classic technique to estimate causal effects ...
research
∙
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 ...
research
∙
03/03/2022
Identification in Tree-shaped Linear Structural Causal Models
Linear structural equation models represent direct causal effects as dir...
research
∙
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...
research
∙
10/06/2020
Recovering Causal Structures from Low-Order Conditional Independencies
One of the common obstacles for learning causal models from data is that...
research
∙
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...
research
∙
01/24/2018
On the Gold Standard for Security of Universal Steganography
While symmetric-key steganography is quite well understood both in the i...
research
∙
08/02/2015
Learning from Pairwise Marginal Independencies
We consider graphs that represent pairwise marginal independencies among...
research
∙
02/14/2012
