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Found 18 papers, 2 papers with code
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.
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.
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.
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 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.
Non-cooperative Multi-agent Systems with Exploring Agents
This leads to a set of coupled Bellman equations that describes the behavior of the agents.
Causal Transfer for Imitation Learning and Decision Making under Sensor-shift
Learning from demonstrations (LfD) is an efficient paradigm to train AI agents.
Wasserstein Adversarial Imitation Learning
Imitation Learning describes the problem of recovering an expert policy from demonstrations.
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.
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).
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.
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.
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.
Efficient Neighborhood Selection for Gaussian Graphical Models
This paper addresses the problem of neighborhood selection for Gaussian graphical models.
