no code implementations • 31 Mar 2024 • AmirEmad Ghassami
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.
no code implementations • 15 Nov 2023 • Zixiao Wang, AmirEmad Ghassami, Ilya Shpitser
We consider the task of identifying and estimating a parameter of interest in settings where data is missing not at random (MNAR).
no code implementations • 10 Apr 2023 • AmirEmad Ghassami, Ilya Shpitser, Eric Tchetgen Tchetgen
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.
1 code implementation • 8 Nov 2022 • Yuqin Yang, AmirEmad Ghassami, Mohamed Nafea, Negar Kiyavash, Kun Zhang, Ilya Shpitser
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.
1 code implementation • 20 May 2022 • Ehsan Mokhtarian, Saber Salehkaleybar, AmirEmad Ghassami, Negar Kiyavash
We study experiment design for unique identification of the causal graph of a simple SCM, where the graph may contain cycles.
no code implementations • 26 Jan 2022 • AmirEmad Ghassami, Alan Yang, David Richardson, Ilya Shpitser, Eric Tchetgen Tchetgen
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.
1 code implementation • 4 Nov 2021 • AmirEmad Ghassami, Alan Yang, Ilya Shpitser, Eric Tchetgen Tchetgen
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.
1 code implementation • 30 Oct 2021 • Yuqin Yang, Mohamed Nafea, AmirEmad Ghassami, Negar Kiyavash
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.
no code implementations • 24 Oct 2021 • AmirEmad Ghassami, Ilya Shpitser
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.
1 code implementation • NeurIPS 2021 • Sina Akbari, Ehsan Mokhtarian, AmirEmad Ghassami, Negar Kiyavash
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.
no code implementations • 1 Jun 2021 • Sajad Khodadadian, Mohamed Nafea, AmirEmad Ghassami, Negar Kiyavash
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.
no code implementations • 19 May 2021 • Numair Sani, Yizhen Xu, AmirEmad Ghassami, Ilya Shpitser
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.
1 code implementation • 7 Apr 2021 • AmirEmad Ghassami, Andrew Ying, Ilya Shpitser, Eric Tchetgen Tchetgen
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.
no code implementations • 3 Feb 2021 • Sajad Khodadadian, AmirEmad Ghassami, Negar Kiyavash
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.
1 code implementation • 10 Oct 2020 • Ehsan Mokhtarian, Sina Akbari, AmirEmad Ghassami, Negar Kiyavash
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.
1 code implementation • NeurIPS 2020 • Ignavier Ng, AmirEmad Ghassami, Kun Zhang
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.
1 code implementation • 12 Nov 2019 • Alan Yang, AmirEmad Ghassami, Maxim Raginsky, Negar Kiyavash, Elyse Rosenbaum
In the second step, CI testing is performed by applying the $k$-NN conditional mutual information estimator to the learned feature maps.
1 code implementation • ICML 2020 • AmirEmad Ghassami, Alan Yang, Negar Kiyavash, Kun Zhang
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.
no code implementations • 12 Oct 2019 • AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash
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.
no code implementations • 11 Aug 2019 • Saber Salehkaleybar, AmirEmad Ghassami, Negar Kiyavash, Kun Zhang
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.
no code implementations • NeurIPS 2018 • Amiremad Ghassami, Negar Kiyavash, Biwei Huang, Kun Zhang
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.
no code implementations • 4 Jun 2018 • Chien-Ying Chen, Monowar Hasan, AmirEmad Ghassami, Sibin Mohan, Negar Kiyavash
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
no code implementations • 5 Feb 2018 • AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Kun Zhang
In this paper, we propose a new technique for counting the number of DAGs in a Markov equivalence class.
no code implementations • 13 Jan 2018 • AmirEmad Ghassami, Sajad Khodadadian, Negar Kiyavash
To ensure fairness and generalization simultaneously, we compress the data to an auxiliary variable, which is used for the prediction task.
no code implementations • ICML 2018 • AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Elias Bareinboim
We study the problem of causal structure learning when the experimenter is limited to perform at most $k$ non-adaptive experiments of size $1$.
no code implementations • NeurIPS 2017 • AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Kun Zhang
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.
no code implementations • 27 Feb 2017 • AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash
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.
no code implementations • 30 Jan 2017 • AmirEmad Ghassami, Negar Kiyavash
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.