no code implementations • 6 Dec 2023 • Henrik von Kleist, Alireza Zamanian, Ilya Shpitser, Narges Ahmidi
These estimators can be applied when the missingness in the retrospective dataset follows a missing-at-random (MAR) pattern.
no code implementations • 3 Dec 2023 • Henrik von Kleist, Alireza Zamanian, Ilya Shpitser, Narges Ahmidi
We show that one can apply offline reinforcement learning under the NUC assumption and missing data methods under the NDE assumption.
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 • 7 Jul 2023 • Ilya Shpitser
The ID algorithm is sound (outputs the correct functional of the observed data distribution whenever p(Y | do(a)) is identified in the causal model represented by the input graph), and complete (explicitly flags as a failure any input p(Y | do(a)) whenever this distribution is not identified in the causal model represented by the input graph).
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.
no code implementations • 11 Oct 2022 • Razieh Nabi, Rohit Bhattacharya, Ilya Shpitser, James Robins
It is often said that the fundamental problem of causal inference is a missing data problem -- the comparison of responses to two hypothetical treatment assignments is made difficult because for every experimental unit only one potential response is observed.
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.
no code implementations • 1 Dec 2021 • Nathan Drenkow, Numair Sani, Ilya Shpitser, Mathias Unberath
We find this area of research has received disproportionately less attention relative to adversarial machine learning, yet a significant robustness gap exists that manifests in performance degradation similar in magnitude to adversarial conditions.
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.
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.
no code implementations • 28 Sep 2021 • Guilherme Duarte, Noam Finkelstein, Dean Knox, Jonathan Mummolo, Ilya Shpitser
When causal quantities cannot be point identified, researchers often pursue partial identification to quantify the range of possible values.
no code implementations • 15 Aug 2021 • Ilya Shpitser, Zach Wood-Doughty, Eric J. Tchetgen Tchetgen
Unobserved confounding is a fundamental obstacle to establishing valid causal conclusions from observational data.
no code implementations • 15 Jul 2021 • Noam Finkelstein, Beata Zjawin, Elie Wolfe, Ilya Shpitser, Robert W. Spekkens
Directed acyclic graphs (DAGs) with hidden variables are often used to characterize causal relations between variables in a system.
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 • 10 Feb 2021 • Zach Wood-Doughty, Ilya Shpitser, Mark Dredze
High-dimensional and unstructured data such as natural language complicates the evaluation of causal inference methods; such evaluations rely on synthetic datasets with known causal effects.
no code implementations • 23 Dec 2020 • Noam Finkelstein, Roy Adams, Suchi Saria, Ilya Shpitser
When data contains measurement errors, it is necessary to make assumptions relating the observed, erroneous data to the unobserved true phenomena of interest.
1 code implementation • 14 Oct 2020 • Rohit Bhattacharya, Tushar Nagarajan, Daniel Malinsky, Ilya Shpitser
In this work, we derive differentiable algebraic constraints that fully characterize the space of ancestral ADMGs, as well as more general classes of ADMGs, arid ADMGs and bow-free ADMGs, that capture all equality restrictions on the observed variables.
no code implementations • 24 Aug 2020 • Ranjani Srinivasan, Jaron Lee, Rohit Bhattacharya, Narges Ahmidi, Ilya Shpitser
Causal analyses of longitudinal data generally assume that the qualitative causal structure relating variables remains invariant over time.
no code implementations • 13 Aug 2020 • Ilya Shpitser, Thomas S. Richardson, James M. Robins
Among Judea Pearl's many contributions to Causality and Statistics, the graphical d-separation} criterion, the do-calculus and the mediation formula stand out.
Methodology 62P10
no code implementations • 1 Jul 2020 • Noam Finkelstein, Ilya Shpitser
We additionally provide inequality constraints on functionals of the observed data law implied by such causal models.
no code implementations • 8 Jun 2020 • Numair Sani, Jaron Lee, Razieh Nabi, Ilya Shpitser
In order to combat this shortcoming, we propose a novel approach to trading off interpretability and performance in prediction models using ideas from semiparametric statistics, allowing us to combine the interpretability of parametric regression models with performance of nonparametric methods.
no code implementations • 3 Jun 2020 • Numair Sani, Daniel Malinsky, Ilya Shpitser
However, existing approaches have two important shortcomings: (i) the "explanatory units" are micro-level inputs into the relevant prediction model, e. g., image pixels, rather than interpretable macro-level features that are more useful for understanding how to possibly change the algorithm's behavior, and (ii) existing approaches assume there exists no unmeasured confounding between features and target model predictions, which fails to hold when the explanatory units are macro-level variables.
no code implementations • ICML 2020 • Razieh Nabi, Rohit Bhattacharya, Ilya Shpitser
Missing data has the potential to affect analyses conducted in all fields of scientific study, including healthcare, economics, and the social sciences.
no code implementations • 2 Apr 2020 • Eli Sherman, David Arbour, Ilya Shpitser
In many applied fields, researchers are often interested in tailoring treatments to unit-level characteristics in order to optimize an outcome of interest.
no code implementations • 2 Apr 2020 • Jaron J. R. Lee, Ilya Shpitser
This entails using \emph{identification theory} to establish a link between counterfactual parameters of interest and distributions from which data is available.
no code implementations • 27 Mar 2020 • Rohit Bhattacharya, Razieh Nabi, Ilya Shpitser
We derive influence function based estimators that exhibit double robustness for the identified effects in a large class of hidden variable DAGs where the treatment satisfies a simple graphical criterion; this class includes models yielding the adjustment and front-door functionals as special cases.
no code implementations • 17 Jan 2020 • Elizabeth L. Ogburn, Ilya Shpitser, Eric J. Tchetgen Tchetgen
This note has been updated (April, 2020) to respond to "Towards Clarifying the Theory of the Deconfounder" by Yixin Wang, David M. Blei (arXiv:2003. 04948).
no code implementations • 11 Oct 2019 • Elizabeth L. Ogburn, Ilya Shpitser, Eric J. Tchetgen Tchetgen
(This comment has been updated to respond to Wang and Blei's rejoinder [arXiv:1910. 07320].)
no code implementations • 9 Oct 2019 • Razieh Nabi, Daniel Malinsky, Ilya Shpitser
Specifically, we show how to reparameterize the observed data likelihood such that fairness constraints correspond directly to parameters that appear in the likelihood, transforming a complex constrained optimization objective into a simple optimization problem with box constraints.
no code implementations • 29 Jun 2019 • Rohit Bhattacharya, Razieh Nabi, Ilya Shpitser, James M. Robins
Missing data is a pervasive problem in data analyses, resulting in datasets that contain censored realizations of a target distribution.
no code implementations • 29 Jun 2019 • Rohit Bhattacharya, Daniel Malinsky, Ilya Shpitser
Classical causal and statistical inference methods typically assume the observed data consists of independent realizations.
no code implementations • 20 May 2019 • Daniel Chicharro, Stefano Panzeri, Ilya Shpitser
Methods based on additive-noise (AN) models have been proposed to further discriminate between causal structures that are equivalent in terms of conditional independencies.
3 code implementations • 12 Dec 2018 • Elizabeth L. Ogburn, Ilya Shpitser, Youjin Lee
Traditionally, statistical and causal inference on human subjects relies on the assumption that individuals are independently affected by treatments or exposures.
Methodology
no code implementations • NeurIPS 2018 • Eli Sherman, Ilya Shpitser
The assumption that data samples are independent and identically distributed (iid) is standard in many areas of statistics and machine learning.
1 code implementation • EMNLP 2018 • Zach Wood-Doughty, Ilya Shpitser, Mark Dredze
Causal understanding is essential for many kinds of decision-making, but causal inference from observational data has typically only been applied to structured, low-dimensional datasets.
no code implementations • 27 Sep 2018 • Razieh Nabi, Phyllis Kanki, Ilya Shpitser
For example, we may wish to maximize the chemical effect of a drug given data from an observational study where the chemical effect of the drug on the outcome is entangled with the indirect effect mediated by differential adherence.
no code implementations • 6 Sep 2018 • Razieh Nabi, Daniel Malinsky, Ilya Shpitser
Systematic discriminatory biases present in our society influence the way data is collected and stored, the way variables are defined, and the way scientific findings are put into practice as policy.
no code implementations • 29 May 2017 • Razieh Nabi, Ilya Shpitser
We discuss a number of complications that arise in classical statistical inference due to this view and provide workarounds based on recent work in causal and semi-parametric inference.
no code implementations • NeurIPS 2016 • Ilya Shpitser
Our estimators, which are generalized inverse probability weighting estimators, make no assumptions on the underlying full data law, but instead place independence restrictions, and certain other fairly mild assumptions, on the distribution of missingness status conditional on the data.
no code implementations • NeurIPS 2015 • Ilya Shpitser
Our results suggest that segregated graphs define an analogue of the ordinary Markov model for marginals of chain graph models.
no code implementations • 26 Sep 2013 • Ilya Shpitser, Robin J. Evans, Thomas S. Richardson, James M. Robins
To make modeling and inference with nested Markov models practical, it is necessary to limit the number of parameters in the model, while still correctly capturing the constraints in the marginal of a DAG model.
no code implementations • 2 Apr 2013 • Tyler J. VanderWeele, Ilya Shpitser
The literature has not, however, come to any consensus on a formal definition of a confounder, as it has given priority to the concept of confounding over that of a confounder.
1 code implementation • 20 Jun 2012 • Ilya Shpitser, Judea Pearl
Counterfactual statements, e. g., "my headache would be gone had I taken an aspirin" are central to scientific discourse, and are formally interpreted as statements derived from "alternative worlds".