Search Results for author:
Found 16 papers, 8 papers with code
Establishing Markov Equivalence in Cyclic Directed Graphs
We present a new, efficient procedure to establish Markov equivalence between directed graphs that may or may not contain cycles under the \textit{d}-separation criterion.
Towards a Benchmark for Scientific Understanding in Humans and Machines
We extend this notion by considering a set of questions that can gauge different levels of scientific understanding, covering information retrieval, the capability to arrange information to produce an explanation, and the ability to infer how things would be different under different circumstances.
Spectral Ranking of Causal Influence in Complex Systems
Like natural complex systems such as the Earth's climate or a living cell, semiconductor lithography systems are characterized by nonlinear dynamics across more than a dozen orders of magnitude in space and time.
Time Series Analysis
Information Theory
Information Theory
Adaptation and Self-Organizing Systems
Physics and Society
Inferring the Direction of a Causal Link and Estimating Its Effect via a Bayesian Mendelian Randomization Approach
In this paper, we propose a Bayesian approach called BayesMR that is a generalization of the Mendelian randomization technique in which we allow for pleiotropic effects and, crucially, for the possibility of reverse causation.
MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models
Unfortunately, searching for proper instruments in a many-dimensional set of candidates is a daunting task due to the intractable model space and the fact that we cannot directly test which of these candidates are valid, so most existing search methods either rely on overly stringent modeling assumptions or fail to capture the inherent model uncertainty in the selection process.
Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models
Shapley values underlie one of the most popular model-agnostic methods within explainable artificial intelligence.
Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles
While feedback loops are known to play important roles in many complex systems, their existence is ignored in a large part of the causal discovery literature, as systems are typically assumed to be acyclic from the outset.
Large-Scale Local Causal Inference of Gene Regulatory Relationships
Many of these computational methods are designed to infer individual regulatory relationships among genes from data on gene expression.
A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks
Gene regulatory networks play a crucial role in controlling an organism's biological processes, which is why there is significant interest in developing computational methods that are able to extract their structure from high-throughput genetic data.
Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions
An important goal common to domain adaptation and causal inference is to make accurate predictions when the distributions for the source (or training) domain(s) and target (or test) domain(s) differ.
Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness
Causal effect estimation from observational data is an important and much studied research topic.
Joint Causal Inference from Multiple Contexts
We explain how several well-known causal discovery algorithms can be seen as addressing special cases of the JCI framework, and we also propose novel implementations that extend existing causal discovery methods for purely observational data to the JCI setting.
Ancestral Causal Inference
Constraint-based causal discovery from limited data is a notoriously difficult challenge due to the many borderline independence test decisions.
Proof Supplement - Learning Sparse Causal Models is not NP-hard (UAI2013)
The algorithm is an adaptation of the well-known FCI algorithm by (Spirtes et al., 2000) that is also sound and complete, but has worst case complexity exponential in $N$.
Learning Sparse Causal Models is not NP-hard
This paper shows that causal model discovery is not an NP-hard problem, in the sense that for sparse graphs bounded by node degree k the sound and complete causal model can be obtained in worst case order N^{2(k+2)} independence tests, even when latent variables and selection bias may be present.
Causal discovery in multiple models from different experiments
We present the MCI-algorithm as the first method that can infer provably valid causal relations in the large sample limit from different experiments.
