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no code implementations • ICML 2020 • Daniel Kumor, Carlos Cinelli, Elias Bareinboim

We develop a a new polynomial-time algorithm for identification in linear Structural Causal Models that subsumes previous non-exponential identification methods when applied to direct effects, and unifies several disparate approaches to identification in linear systems.

no code implementations • ICML 2020 • Sanghack Lee, Elias Bareinboim

Finally, building on these graphical properties, we develop an algorithm that returns a formula for a causal effect in terms of the available distributions.

1 code implementation • 11 Oct 2022 • Hyunchai Jeong, Jin Tian, Elias Bareinboim

Identifying the effects of new interventions from data is a significant challenge found across a wide range of the empirical sciences.

no code implementations • 30 Sep 2022 • Kevin Xia, Yushu Pan, Elias Bareinboim

We show that this algorithm is sound and complete for deciding counterfactual identification in general settings.

no code implementations • NeurIPS 2020 • Junzhe Zhang, Daniel Kumor, Elias Bareinboim

One of the common ways children learn is by mimicking adults.

no code implementations • NeurIPS 2021 • Daniel Kumor, Junzhe Zhang, Elias Bareinboim

"Monkey see monkey do" is an age-old adage, referring to na\"ive imitation without a deep understanding of a system's underlying mechanics.

no code implementations • 23 Jul 2022 • Drago Plecko, Elias Bareinboim

The main insight of our approach will be to link the quantification of the disparities present on the observed data with the underlying, and often unobserved, collection of causal mechanisms that generate the disparity in the first place, challenge we call the Fundamental Problem of Causal Fairness Analysis (FPCFA).

1 code implementation • CVPR 2022 • Chengzhi Mao, Kevin Xia, James Wang, Hao Wang, Junfeng Yang, Elias Bareinboim, Carl Vondrick

Visual representations underlie object recognition tasks, but they often contain both robust and non-robust features.

no code implementations • 22 Feb 2022 • Tara V. Anand, Adèle H. Ribeiro, Jin Tian, Elias Bareinboim

In this paper, we introduce a new type of graphical model called cluster causal diagrams (for short, C-DAGs) that allows for the partial specification of relationships among variables based on limited prior knowledge, alleviating the stringent requirement of specifying a full causal diagram.

no code implementations • NeurIPS 2021 • Yonghan Jung, Jin Tian, Elias Bareinboim

We study the problem of estimating the density of the causal effect of a binary treatment on a continuous outcome given a binary instrumental variable in the presence of covariates.

no code implementations • NeurIPS 2021 • Sanghack Lee, Elias Bareinboim

Causal effect identification is concerned with determining whether a causal effect is computable from a combination of qualitative assumptions about the underlying system (e. g., a causal graph) and distributions collected from this system.

no code implementations • 12 Oct 2021 • Junzhe Zhang, Jin Tian, Elias Bareinboim

This paper investigates the problem of bounding counterfactual queries from an arbitrary collection of observational and experimental distributions and qualitative knowledge about the underlying data-generating model represented in the form of a causal diagram.

no code implementations • NeurIPS 2021 • Juan D Correa, Sanghack Lee, Elias Bareinboim

In this paper, we study the identification of nested counterfactuals from an arbitrary combination of observations and experiments.

2 code implementations • NeurIPS 2021 • Kevin Xia, Kai-Zhan Lee, Yoshua Bengio, Elias Bareinboim

Given this property, one may be tempted to surmise that a collection of neural nets is capable of learning any SCM by training on data generated by that SCM.

no code implementations • NeurIPS 2021 • Junzhe Zhang, Elias Bareinboim, Jin Tian

We show that all counterfactual distributions (over finite observed variables) in an arbitrary causal diagram could be generated by a special family of structural causal models (SCMs), compatible with the same causal diagram, where unobserved (exogenous) variables are discrete, taking values in a finite domain.

no code implementations • NeurIPS 2020 • Amin Jaber, Murat Kocaoglu, Karthikeyan Shanmugam, Elias Bareinboim

One fundamental problem in the empirical sciences is of reconstructing the causal structure that underlies a phenomenon of interest through observation and experimentation.

no code implementations • NeurIPS 2020 • Sanghack Lee, Elias Bareinboim

Intelligent agents are continuously faced with the challenge of optimizing a policy based on what they can observe (see) and which actions they can take (do) in the environment where they are deployed.

no code implementations • NeurIPS 2020 • Yonghan Jung, Jin Tian, Elias Bareinboim

In this paper, we develop a learning framework that marries two families of methods, benefiting from the generality of the causal identification theory and the effectiveness of the estimators produced based on the principle of ERM.

no code implementations • NeurIPS 2020 • Juan Correa, Elias Bareinboim

As a corollary, we show that the $\sigma$-calculus is complete for the task of soft-transportability.

no code implementations • 19 Dec 2019 • Paul Hünermund, Elias Bareinboim

Learning about cause and effect is arguably the main goal in applied econometrics.

Econometrics

no code implementations • NeurIPS 2019 • Junzhe Zhang, Elias Bareinboim

A dynamic treatment regime (DTR) consists of a sequence of decision rules, one per stage of intervention, that dictates how to determine the treatment assignment to patients based on evolving treatments and covariates' history.

no code implementations • NeurIPS 2019 • Murat Kocaoglu, Amin Jaber, Karthikeyan Shanmugam, Elias Bareinboim

We introduce a novel notion of interventional equivalence class of causal graphs with latent variables based on these invariances, which associates each graphical structure with a set of interventional distributions that respect the do-calculus rules.

no code implementations • NeurIPS 2019 • Amin Jaber, Jiji Zhang, Elias Bareinboim

A generalization of this problem restricts the qualitative knowledge to a class of Markov equivalent causal diagrams, which, unlike a single, fully-specified causal diagram, can be inferred from the observational distribution.

1 code implementation • NeurIPS 2019 • Daniel Kumor, Bryant Chen, Elias Bareinboim

Building on the literature of instrumental variables (IVs), a plethora of methods has been developed to identify causal effects in linear systems.

no code implementations • 15 Dec 2018 • Amin Jaber, Jiji Zhang, Elias Bareinboim

The problem of identification of causal effects is concerned with determining whether a causal effect can be computed from a combination of observational data and substantive knowledge about the domain under investigation, which is formally expressed in the form of a causal graph.

1 code implementation • NeurIPS 2018 • Sanghack Lee, Elias Bareinboim

We study the problem of identifying the best action in a sequential decision-making setting when the reward distributions of the arms exhibit a non-trivial dependence structure, which is governed by the underlying causal model of the domain where the agent is deployed.

no code implementations • NeurIPS 2018 • Junzhe Zhang, Elias Bareinboim

The goal of this paper is to develop a principled approach to connect the statistical disparities characterized by the EO and the underlying, elusive, and frequently unobserved, causal mechanisms that generated such inequality.

no code implementations • NeurIPS 2017 • Murat Kocaoglu, Karthikeyan Shanmugam, Elias Bareinboim

Next, we propose an algorithm that uses only O(d^2 log n) interventions that can learn the latents between both non-adjacent and adjacent variables.

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 • ICML 2017 • Bryant Chen, Daniel Kumor, Elias Bareinboim

In this paper, we provide an algorithm for the identification of causal parameters in linear structural models that subsumes previous state-of-the-art methods.

no code implementations • ICML 2017 • Andrew Forney, Judea Pearl, Elias Bareinboim

The Multi-Armed Bandit problem with Unobserved Confounders (MABUC) considers decision-making settings where unmeasured variables can influence both the agent’s decisions and received rewards (Bareinboim et al., 2015).

no code implementations • NeurIPS 2015 • Elias Bareinboim, Andrew Forney, Judea Pearl

The Multi-Armed Bandit problem constitutes an archetypal setting for sequential decision-making, permeating multiple domains including engineering, business, and medicine.

no code implementations • 10 Nov 2015 • Bryant Chen, Judea Pearl, Elias Bareinboim

This cancellation allows the auxiliary variables to help conventional methods of identification (e. g., single-door criterion, instrumental variables, half-trek criterion), as well as model testing (e. g., d-separation, over-identification).

no code implementations • 5 Mar 2015 • Judea Pearl, Elias Bareinboim

The generalizability of empirical findings to new environments, settings or populations, often called "external validity," is essential in most scientific explorations.

no code implementations • NeurIPS 2014 • Elias Bareinboim, Judea Pearl

This paper addresses the problem of $mz$-transportability, that is, transferring causal knowledge collected in several heterogeneous domains to a target domain in which only passive observations and limited experimental data can be collected.

no code implementations • 29 Dec 2013 • Elias Bareinboim, Judea Pearl

Generalizing empirical findings to new environments, settings, or populations is essential in most scientific explorations.

no code implementations • NeurIPS 2013 • Elias Bareinboim, Sanghack Lee, Vasant Honavar, Judea Pearl

This paper considers the problem of transferring experimental findings learned from multiple heterogeneous domains to a target environment, in which only limited experiments can be performed.

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