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Found 39 papers, 6 papers with code
Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets
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.
Causal Effect Identifiability under Partial-Observability
Finally, building on these graphical properties, we develop an algorithm that returns a formula for a causal effect in terms of the available distributions.
Neural Causal Abstractions
The abilities of humans to understand the world in terms of cause and effect relationships, as well as to compress information into abstract concepts, are two hallmark features of human intelligence.
Reconciling Predictive and Statistical Parity: A Causal Approach
Since the rise of fair machine learning as a critical field of inquiry, many different notions on how to quantify and measure discrimination have been proposed in the literature.
Finding and Listing Front-door Adjustment Sets
Identifying the effects of new interventions from data is a significant challenge found across a wide range of the empirical sciences.
Neural Causal Models for Counterfactual Identification and Estimation
We show that this algorithm is sound and complete for deciding counterfactual identification in general settings.
Causal Imitation Learning with Unobserved Confounders
One of the common ways children learn is by mimicking adults.
Sequential Causal Imitation Learning with Unobserved Confounders
"Monkey see monkey do" is an age-old adage, referring to na\"ive imitation without a deep understanding of a system's underlying mechanics.
Causal Fairness Analysis
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).
Causal Transportability for Visual Recognition
Visual representations underlie object recognition tasks, but they often contain both robust and non-robust features.
Causal Effect Identification in Cluster DAGs
Finally, we show that C-DAGs are valid for performing counterfactual inferences about clusters of variables.
Causal Identification with Matrix Equations
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.
Double Machine Learning Density Estimation for Local Treatment Effects with Instruments
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.
Partial Counterfactual Identification from Observational and Experimental Data
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.
Nested Counterfactual Identification from Arbitrary Surrogate Experiments
In this paper, we study the identification of nested counterfactuals from an arbitrary combination of observations and experiments.
The Causal-Neural Connection: Expressiveness, Learnability, and Inference
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.
Partial Identification of Counterfactual Distributions
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.
Characterizing Optimal Mixed Policies: Where to Intervene and What to Observe
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.
Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning
One fundamental problem in the empirical sciences is of reconstructing the causal structure that underlies a phenomenon of interest through observation and experimentation.
General Transportability of Soft Interventions: Completeness Results
As a corollary, we show that the $\sigma$-calculus is complete for the task of soft-transportability.
Learning Causal Effects via Weighted Empirical Risk Minimization
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.
Causal Inference and Data Fusion in Econometrics
Learning about cause and effect is arguably the main goal in applied econometrics.
Near-Optimal Reinforcement Learning in Dynamic Treatment Regimes
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.
Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions
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.
Identification of Conditional Causal Effects under Markov Equivalence
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.
Efficient Identification in Linear Structural Causal Models with Instrumental Cutsets
Building on the literature of instrumental variables (IVs), a plethora of methods has been developed to identify causal effects in linear systems.
Causal Identification under Markov Equivalence
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.
Structural Causal Bandits: Where to Intervene?
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.
Equality of Opportunity in Classification: A Causal Approach
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.
Experimental Design for Learning Causal Graphs with Latent Variables
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.
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 $1$.
Identification and Model Testing in Linear Structural Equation Models using Auxiliary Variables
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.
Counterfactual Data-Fusion for Online Reinforcement Learners
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).
Bandits with Unobserved Confounders: A Causal Approach
The Multi-Armed Bandit problem constitutes an archetypal setting for sequential decision-making, permeating multiple domains including engineering, business, and medicine.
Incorporating Knowledge into Structural Equation Models using Auxiliary Variables
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).
External Validity: From Do-Calculus to Transportability Across Populations
The generalizability of empirical findings to new environments, settings or populations, often called "external validity," is essential in most scientific explorations.
Transportability from Multiple Environments with Limited Experiments: Completeness Results
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.
A General Algorithm for Deciding Transportability of Experimental Results
Generalizing empirical findings to new environments, settings, or populations is essential in most scientific explorations.
Transportability from Multiple Environments with Limited Experiments
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.
