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Found 28 papers, 10 papers with code
Two-Stage Nuisance Function Estimation for Causal Mediation Analysis
In this work, we propose a two-stage estimation strategy for the nuisance functions that estimates the nuisance functions based on the role they play in the structure of the bias of the influence function-based estimator of the mediation functional.
Identification and Estimation for Nonignorable Missing Data: A Data Fusion Approach
We consider the task of identifying and estimating a parameter of interest in settings where data is missing not at random (MNAR).
Partial Identification of Causal Effects Using Proxy Variables
However, completeness is well-known not to be empirically testable, and although a bridge function may be well-defined, lack of completeness, sometimes manifested by availability of a single type of proxy, may severely limit prospects for identification of a bridge function and thus a causal effect; therefore, potentially restricting the application of the proximal causal framework.
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.
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.
Combining Experimental and Observational Data for Identification and Estimation of Long-Term Causal Effects
We consider the task of identifying and estimating the causal effect of a treatment variable on a long-term outcome variable using data from an observational domain and an experimental domain.
Causal Inference with Hidden Mediators
In this paper, we extend the proximal causal inference approach to settings where identification of causal effects hinges upon a set of mediators which are not observed, yet error prone proxies of the hidden mediators are measured.
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.
Partially Intervenable Causal Models
We give a complete identification theory for such models, and develop a complete calculus of interventions based on a generalization of the do-calculus, and axioms that govern probabilistic operations on Markov kernels.
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.
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.
Multiply Robust Causal Mediation Analysis with Continuous Treatments
For binary treatments, efficient estimators for the direct and indirect effects are presented in Tchetgen Tchetgen and Shpitser (2012) based on the influence function of the parameter of interest.
Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inference
In this paper, we first extend the class of Robins et al. to include doubly robust IFs in which the nuisance functions are solutions to integral equations.
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.
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.
On the Role of Sparsity and DAG Constraints for Learning Linear DAGs
Extensive experiments validate the effectiveness of our proposed method and show that the DAG-penalized likelihood objective is indeed favorable over the least squares one with the hard DAG constraint.
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.
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.
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
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.
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.
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.
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.
