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Found 58 papers, 13 papers with code
Recursive Causal Discovery
Presence and identification of removable variables allow recursive approaches for causal discovery, a promising solution that helps to address the aforementioned challenges by reducing the problem size successively.
Confounded Budgeted Causal Bandits
This algorithm generalizes the state-of-the-art methods by allowing non-uniform costs and hidden confounders in the causal graph.
Modeling Systemic Risk: A Time-Varying Nonparametric Causal Inference Framework
We propose a nonparametric and time-varying directed information graph (TV-DIG) framework to estimate the evolving causal structure in time series networks, thereby addressing the limitations of traditional econometric models in capturing high-dimensional, nonlinear, and time-varying interconnections among series.
Learning Unknown Intervention Targets in Structural Causal Models from Heterogeneous Data
We provide a candidate intervention target set which is a superset of the true intervention targets.
Efficiently Escaping Saddle Points for Non-Convex Policy Optimization
Policy gradient (PG) is widely used in reinforcement learning due to its scalability and good performance.
On sample complexity of conditional independence testing with Von Mises estimator with application to causal discovery
This, in turn, allows us to characterize the sample complexity of any constraint-based causal discovery algorithm that uses VM-CI for CI tests.
s-ID: Causal Effect Identification in a Sub-Population
We introduce and advocate for a causal inference problem in sub-populations (henceforth called s-ID), in which we merely have access to observational data of the targeted sub-population (as opposed to the entire population).
Gaussian Database Alignment and Gaussian Planted Matching
We study an instance of the database alignment problem with multivariate Gaussian features and derive results that apply both for database alignment and for planted matching, demonstrating the connection between them.
On Identifiability of Conditional Causal Effects
It extends the results of [Lee et al., 2019, Kivva et al., 2022] on general identifiability (gID) which studied the problem for unconditional causal effects and Shpitser and Pearl [2006b] on identifiability of conditional causal effects given merely the observational distribution $P(\mathbf{V})$ as our algorithm generalizes the algorithms proposed in [Kivva et al., 2022] and [Shpitser and Pearl, 2006b].
Causal Bandits without Graph Learning
We study the causal bandit problem when the causal graph is unknown and develop an efficient algorithm for finding the parent node of the reward node using atomic interventions.
Causal Discovery in Linear Latent Variable Models Subject to Measurement Error
We demonstrate a somewhat surprising connection between this problem and causal discovery in the presence of unobserved parentless causes, in the sense that there is a mapping, given by the mixing matrix, between the underlying models to be inferred in these problems.
Novel Ordering-based Approaches for Causal Structure Learning in the Presence of Unobserved Variables
We propose ordering-based approaches for learning the maximal ancestral graph (MAG) of a structural equation model (SEM) up to its Markov equivalence class (MEC) in the presence of unobserved variables.
Revisiting the General Identifiability Problem
A nice property of this new algorithm is that it establishes a connection between general identifiability and classical identifiability by Pearl [1995] through decomposing the general identifiability problem into a series of classical identifiability sub-problems.
Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function
We prove that the total sample complexity of SCRN in achieving $\epsilon$-global optimum is $\mathcal{O}(\epsilon^{-7/(2\alpha)+1})$ for $1\le\alpha< 3/2$ and $\mathcal{\tilde{O}}(\epsilon^{-2/(\alpha)})$ for $3/2\le\alpha\le 2$.
A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models
We study experiment design for unique identification of the causal graph of a simple SCM, where the graph may contain cycles.
Momentum-Based Policy Gradient with Second-Order Information
SHARP algorithm is parameter-free, achieving $\epsilon$-approximate first-order stationary point with $O(\epsilon^{-3})$ number of trajectories, while using a batch size of $O(1)$ at each iteration.
Experimental Design for Causal Effect Identification
When such an effect is not identifiable, it is necessary to perform a collection of often costly interventions in the system to learn the causal effect.
Causal Discovery in Linear Structural Causal Models with Deterministic Relations
Linear structural causal models (SCMs)-- in which each observed variable is generated by a subset of the other observed variables as well as a subset of the exogenous sources-- are pervasive in causal inference and casual discovery.
Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Bias
The upper bound of our proposed approach and the lower bound at most differ by a factor equal to the number of variables in the worst case.
Causal Effect Identification with Context-specific Independence Relations of Control Variables
We study the problem of causal effect identification from observational distribution given the causal graph and some context-specific independence (CSI) relations.
Information Theoretic Measures for Fairness-aware Feature Selection
In particular, we first propose information theoretic measures which quantify the impact of different subsets of features on the accuracy and discrimination of the decision outcomes.
The Complexity of Nonconvex-Strongly-Concave Minimax Optimization
In the averaged smooth finite-sum setting, our proposed algorithm improves over previous algorithms by providing a nearly-tight dependence on the condition number.
Impact of Data Processing on Fairness in Supervised Learning
Finally, we show that by appropriate choice of the discrimination measure, the optimization problem for both pre and post processing approaches will reduce to a linear program and hence can be solved efficiently.
The Devil is in the Detail: A Framework for Macroscopic Prediction via Microscopic Models
Macroscopic data aggregated from microscopic events are pervasive in machine learning, such as country-level COVID-19 infection statistics based on city-level data.
A Catalyst Framework for Minimax Optimization
We introduce a generic \emph{two-loop} scheme for smooth minimax optimization with strongly-convex-concave objectives.
Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems
Nonconvex minimax problems appear frequently in emerging machine learning applications, such as generative adversarial networks and adversarial learning.
A Recursive Markov Boundary-Based Approach to Causal Structure Learning
In this paper, we propose a novel recursive constraint-based method for causal structure learning that significantly reduces the required number of CI tests compared to the existing literature.
LazyIter: A Fast Algorithm for Counting Markov Equivalent DAGs and Designing Experiments
We utilize the proposed method for computing MEC sizes and experiment design in active and passive learning settings.
Global Convergence and Variance-Reduced Optimization for a Class of Nonconvex-Nonconcave Minimax Problems
Nonconvex minimax problems appear frequently in emerging machine learning applications, such as generative adversarial networks and adversarial learning.
Toward Optimal Adversarial Policies in the Multiplicative Learning System with a Malicious Expert
We consider a learning system based on the conventional multiplicative weight (MW) rule that combines experts' advice to predict a sequence of true outcomes.
Learning Positive Functions with Pseudo Mirror Descent
The nonparametric learning of positive-valued functions appears widely in machine learning, especially in the context of estimating intensity functions of point processes.
Model-Augmented Estimation of Conditional Mutual Information for Feature Selection
In the second step, CI testing is performed by applying the $k$-NN conditional mutual information estimator to the learned feature maps.
Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs
The main approach to defining equivalence among acyclic directed causal graphical models is based on the conditional independence relationships in the distributions that the causal models can generate, in terms of the Markov equivalence.
Interventional Experiment Design for Causal Structure Learning
For this case, we propose an efficient exact algorithm for the worst-case gain setup, as well as an approximate algorithm for the average gain setup.
Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables
It can be shown that causal effects among observed variables cannot be identified uniquely even under the assumptions of faithfulness and non-Gaussianity of exogenous noises.
Database Alignment with Gaussian Features
We consider the problem of aligning a pair of databases with jointly Gaussian features.
Multi-domain Causal Structure Learning in Linear Systems
We study the problem of causal structure learning in linear systems from observational data given in multiple domains, across which the causal coefficients and/or the distribution of the exogenous noises may vary.
Predictive Approximate Bayesian Computation via Saddle Points
Approximate Bayesian computation (ABC) is an important methodology for Bayesian inference when the likelihood function is intractable.
Partial Recovery of Erdős-Rényi Graph Alignment via $k$-Core Alignment
This estimator searches for an alignment in which the intersection of the correlated graphs using this alignment has a minimum degree of $k$.
REORDER: Securing Dynamic-Priority Real-Time Systems Using Schedule Obfuscation
The deterministic (timing) behavior of real-time systems (RTS) can be used by adversaries - say, to launch side channel attacks or even destabilize the system by denying access to critical resources.
Cryptography and Security
Analysis of a Canonical Labeling Algorithm for the Alignment of Correlated Erdős-Rényi Graphs
Graph alignment in two correlated random graphs refers to the task of identifying the correspondence between vertex sets of the graphs.
Counting and Sampling from Markov Equivalent DAGs Using Clique Trees
In this paper, we propose a new technique for counting the number of DAGs in a Markov equivalence class.
Nonparametric Hawkes Processes: Online Estimation and Generalization Bounds
In this paper, we design a nonparametric online algorithm for estimating the triggering functions of multivariate Hawkes processes.
Fairness in Supervised Learning: An Information Theoretic Approach
To ensure fairness and generalization simultaneously, we compress the data to an auxiliary variable, which is used for the prediction task.
Online Learning for Multivariate Hawkes Processes
We develop a nonparametric and online learning algorithm that estimates the triggering functions of a multivariate Hawkes process (MHP).
Exact alignment recovery for correlated Erdős-Rényi graphs
We consider the problem of perfectly recovering the vertex correspondence between two correlated Erd\H{o}s-R\'enyi (ER) graphs on the same vertex set.
Budgeted Experiment Design for Causal Structure Learning
We study the problem of causal structure learning when the experimenter is limited to perform at most $k$ non-adaptive experiments of size $1$.
Learning Causal Structures Using Regression Invariance
We study causal inference in a multi-environment setting, in which the functional relations for producing the variables from their direct causes remain the same across environments, while the distribution of exogenous noises may vary.
A New Measure of Conditional Dependence
Measuring conditional dependencies among the variables of a network is of great interest to many disciplines.
Learning Vector Autoregressive Models with Latent Processes
We show that the support of transition matrix among the observed processes and lengths of all latent paths between any two observed processes can be identified successfully under some conditions on the VAR model.
Optimal Experiment Design for Causal Discovery from Fixed Number of Experiments
We study the problem of causal structure learning over a set of random variables when the experimenter is allowed to perform at most $M$ experiments in a non-adaptive manner.
Interaction Information for Causal Inference: The Case of Directed Triangle
Interaction information is one of the multivariate generalizations of mutual information, which expresses the amount information shared among a set of variables, beyond the information, which is shared in any proper subset of those variables.
Identifying Nonlinear 1-Step Causal Influences in Presence of Latent Variables
We propose an approach for learning the causal structure in stochastic dynamical systems with a $1$-step functional dependency in the presence of latent variables.
On the Simultaneous Preservation of Privacy and Community Structure in Anonymized Networks
We ask the question "Does there exist a regime where the network cannot be deanonymized perfectly, yet the community structure could be learned?."
Learning Network of Multivariate Hawkes Processes: A Time Series Approach
This paper studies the problem of recovering the causal structure in network of multivariate linear Hawkes processes.
Improved Achievability and Converse Bounds for Erdős-Rényi Graph Matching
For a pair of correlated graphs on the same vertex set, the correspondence between the vertices can be obscured by randomly permuting the vertex labels of one of the graphs.
Efficient Neighborhood Selection for Gaussian Graphical Models
This paper addresses the problem of neighborhood selection for Gaussian graphical models.
Directed Information Graphs
We show that under appropriate conditions, it is unique and consistent with another type of graphical model, the directed information graph, which is based on a generalization of Granger causality.
