Search Results for author:
Found 45 papers, 7 papers with code
Evaluation of Active Feature Acquisition Methods for Static Feature Settings
These estimators can be applied when the missingness in the retrospective dataset follows a missing-at-random (MAR) pattern.
Evaluation of Active Feature Acquisition Methods for Time-varying Feature Settings
We show that one can apply offline reinforcement learning under the NUC assumption and missing data methods under the NDE assumption.
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).
When does the ID algorithm fail?
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).
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.
Causal and counterfactual views of missing data models
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.
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.
A Systematic Review of Robustness in Deep Learning for Computer Vision: Mind the gap?
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.
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.
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.
An Automated Approach to Causal Inference in Discrete Settings
When causal quantities cannot be point identified, researchers often pursue partial identification to quantify the range of possible values.
The Proximal ID Algorithm
Unobserved confounding is a fundamental obstacle to establishing valid causal conclusions from observational data.
Entropic Inequality Constraints from $e$-separation Relations in Directed Acyclic Graphs with Hidden Variables
Directed acyclic graphs (DAGs) with hidden variables are often used to characterize causal relations between variables in a system.
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.
Generating Synthetic Text Data to Evaluate Causal Inference Methods
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.
Partial Identifiability in Discrete Data With Measurement Error
When data contains measurement errors, it is necessary to make assumptions relating the observed, erroneous data to the unobserved true phenomena of interest.
Differentiable Causal Discovery Under Unmeasured Confounding
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.
Path Dependent Structural Equation Models
Causal analyses of longitudinal data generally assume that the qualitative causal structure relating variables remains invariant over time.
Multivariate Counterfactual Systems And Causal Graphical Models
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
Deriving Bounds and Inequality Constraints Using LogicalRelations Among Counterfactuals
We additionally provide inequality constraints on functionals of the observed data law implied by such causal models.
A Semiparametric Approach to Interpretable Machine Learning
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.
Explaining the Behavior of Black-Box Prediction Algorithms with Causal Learning
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.
Full Law Identification In Graphical Models Of Missing Data: Completeness Results
Missing data has the potential to affect analyses conducted in all fields of scientific study, including healthcare, economics, and the social sciences.
General Identification of Dynamic Treatment Regimes Under Interference
In many applied fields, researchers are often interested in tailoring treatments to unit-level characteristics in order to optimize an outcome of interest.
Identification Methods With Arbitrary Interventional Distributions as Inputs
This entails using \emph{identification theory} to establish a link between counterfactual parameters of interest and distributions from which data is available.
Semiparametric Inference For Causal Effects In Graphical Models With Hidden Variables
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.
Counterexamples to "The Blessings of Multiple Causes" by Wang and Blei
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).
Comment on "Blessings of Multiple Causes"
(This comment has been updated to respond to Wang and Blei's rejoinder [arXiv:1910. 07320].)
Optimal Training of Fair Predictive Models
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.
Identification In Missing Data Models Represented By Directed Acyclic Graphs
Missing data is a pervasive problem in data analyses, resulting in datasets that contain censored realizations of a target distribution.
Causal Inference Under Interference And Network Uncertainty
Classical causal and statistical inference methods typically assume the observed data consists of independent realizations.
Conditionally-additive-noise Models for Structure Learning
Methods based on additive-noise (AN) models have been proposed to further discriminate between causal structures that are equivalent in terms of conditional independencies.
Causal inference, social networks, and chain graphs
Traditionally, statistical and causal inference on human subjects relies on the assumption that individuals are independently affected by treatments or exposures.
Methodology
Identification and Estimation of Causal Effects from Dependent Data
The assumption that data samples are independent and identically distributed (iid) is standard in many areas of statistics and machine learning.
Challenges of Using Text Classifiers for Causal Inference
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.
Estimation of Personalized Effects Associated With Causal Pathways
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.
Learning Optimal Fair Policies
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.
Fair Inference On Outcomes
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.
Consistent Estimation of Functions of Data Missing Non-Monotonically and Not at Random
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.
Segregated Graphs and Marginals of Chain Graph Models
Our results suggest that segregated graphs define an analogue of the ordinary Markov model for marginals of chain graph models.
Sparse Nested Markov models with Log-linear Parameters
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.
On the definition of a confounder
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.
What Counterfactuals Can Be Tested
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".
