Search Results for author:
Found 15 papers, 2 papers with code
Natural Counterfactuals With Necessary Backtracking
Counterfactual reasoning is pivotal in human cognition and especially important for providing explanations and making decisions.
Pathway to Future Symbiotic Creativity
This report presents a comprehensive view of our vision on the development path of the human-machine symbiotic art creation.
Reframed GES with a Neural Conditional Dependence Measure
In this paper, we revisit the Greedy Equivalence Search (GES) algorithm, which is widely cited as a score-based algorithm for learning the MEC of the underlying causal structure.
What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspective
In this paper, we review and reflect on various fairness notions previously proposed in machine learning literature, and make an attempt to draw connections to arguments in moral and political philosophy, especially theories of justice.
Markov categories, causal theories, and the do-calculus
We give a category-theoretic treatment of causal models that formalizes the syntax for causal reasoning over a directed acyclic graph (DAG) by associating a free Markov category with the DAG in a canonical way.
Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions
Many of the causal discovery methods rely on the faithfulness assumption to guarantee asymptotic correctness.
Ancestral Instrument Method for Causal Inference without Complete Knowledge
Based on the theory, we develop an algorithm for unbiased causal effect estimation with a given ancestral IV and observational data.
On Low Rank Directed Acyclic Graphs and Causal Structure Learning
Despite several advances in recent years, learning causal structures represented by directed acyclic graphs (DAGs) remains a challenging task in high dimensional settings when the graphs to be learned are not sparse.
Identification of Conditional Causal Effects under Markov Equivalence
A generalization of this problem restricts the qualitative knowledge to a class of Markov equivalent causal diagrams, which, unlike a single, fully-specified causal diagram, can be inferred from the observational distribution.
ASP-based Discovery of Semi-Markovian Causal Models under Weaker Assumptions
In recent years the possibility of relaxing the so-called Faithfulness assumption in automated causal discovery has been investigated.
Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changes
In this paper, we develop a framework for causal discovery from such data, called Constraint-based causal Discovery from heterogeneous/NOnstationary Data (CD-NOD), to find causal skeleton and directions and estimate the properties of mechanism changes.
Causal Identification under Markov Equivalence
The problem of identification of causal effects is concerned with determining whether a causal effect can be computed from a combination of observational data and substantive knowledge about the domain under investigation, which is formally expressed in the form of a causal graph.
On Learning Causal Structures from Non-Experimental Data without Any Faithfulness Assumption
Then we prove a result to the following effect: for any learning algorithm that tackles the causal learning problem in question, if it achieves the best achievable mode of convergence (considered in this paper), then it must follow the standard design practice of converging to the truth for at least all CBNs that satisfy the faithfulness condition---it is a requirement, not an option.
Discovery and Visualization of Nonstationary Causal Models
Third, we develop a method for visualizing the nonstationarity of causal modules.
Distinguishing Cause from Effect Based on Exogeneity
Recent developments in structural equation modeling have produced several methods that can usually distinguish cause from effect in the two-variable case.
