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Found 40 papers, 3 papers with code
Epsilon-Identifiability of Causal Quantities
Identifying the effects of causes and causes of effects is vital in virtually every scientific field.
Probabilities of Causation: Role of Observational Data
In this paper, we discuss the conditions that observational data are worth considering to improve the quality of the bounds.
Learning Probabilities of Causation from Finite Population Data
This paper deals with the problem of learning the probabilities of causation of subpopulations given finite population data.
Unit Selection: Learning Benefit Function from Finite Population Data
In this paper, we present a machine learning framework that uses the bounds of the benefit function that are estimable from the finite population data to learn the bounds of the benefit function for each cell of characteristics.
Unit Selection: Case Study and Comparison with A/B Test Heuristic
The unit selection problem defined by Li and Pearl identifies individuals who have desired counterfactual behavior patterns, for example, individuals who would respond positively if encouraged and would not otherwise.
Probabilities of Causation: Adequate Size of Experimental and Observational Samples
The assumption is that one is in possession of a large enough sample to permit an accurate estimation of the experimental and observational distributions.
Unit Selection with Nonbinary Treatment and Effect
We propose an algorithm to test the identifiability of the nonbinary benefit function and an algorithm to compute the bounds of the nonbinary benefit function using experimental and observational data.
Personalized Decision Making -- A Conceptual Introduction
Personalized decision making targets the behavior of a specific individual, while population-based decision making concerns a sub-population resembling that individual.
Probabilities of Causation with Nonbinary Treatment and Effect
This paper deals with the problem of estimating the probabilities of causation when treatment and effect are not binary.
Unit Selection with Causal Diagram
The unit selection problem aims to identify a set of individuals who are most likely to exhibit a desired mode of behavior, for example, selecting individuals who would respond one way if encouraged and a different way if not encouraged.
Bounds on Causal Effects and Application to High Dimensional Data
This paper addresses the problem of estimating causal effects when adjustment variables in the back-door or front-door criterion are partially observed.
Causes of Effects: Learning individual responses from population data
The problem of individualization is recognized as crucial in almost every field.
Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution
Current machine learning systems operate, almost exclusively, in a statistical, or model-free mode, which entails severe theoretical limits on their power and performance.
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.
Graphical Models for Recovering Probabilistic and Causal Queries from Missing Data
We address the problem of deciding whether a causal or probabilistic query is estimable from data corrupted by missing entries, given a model of missingness process.
Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data
In contrast to textbook approaches such as EM and the gradient method, our approach is non-iterative, yields closed form parameter estimates, and eliminates the need for inference in a Bayesian network.
Logarithmic-Time Updates and Queries in Probabilistic Networks
In this paper we propose a dynamic data structure that supports efficient algorithms for updating and querying singly connected Bayesian networks (causal trees and polytrees).
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.
Graphical Models for Inference with Missing Data
We address the problem of deciding whether there exists a consistent estimator of a given relation Q, when data are missing not at random.
Deciding Consistency of Databases Containing Defeasible and Strict Information
We propose a norm of consistency for a mixed set of defeasible and strict sentences, based on a probabilistic semantics.
Learning Link-Probabilities in Causal Trees
A learning algorithm is presented which given the structure of a causal tree, will estimate its link probabilities by sequential measurements on the leaves only.
A Constraint Propagation Approach to Probabilistic Reasoning
The paper demonstrates that strict adherence to probability theory does not preclude the use of concurrent, self-activated constraint-propagation mechanisms for managing uncertainty.
Structuring Causal Tree Models with Continuous Variables
This paper considers the problem of invoking auxiliary, unobservable variables to facilitate the structuring of causal tree models for a given set of continuous variables.
Causal Networks: Semantics and Expressiveness
Dependency knowledge of the form "x is independent of y once z is known" invariably obeys the four graphoid axioms, examples include probabilistic and database dependencies.
On the Logic of Causal Models
This paper explores the role of Directed Acyclic Graphs (DAGs) as a representation of conditional independence relationships.
The Recovery of Causal Poly-Trees from Statistical Data
Poly-trees are singly connected causal networks in which variables may arise from multiple causes.
d-Separation: From Theorems to Algorithms
The algorithm runs in time O (l E l) where E is the number of edges in the network.
Do We Need Higher-Order Probabilities and, If So, What Do They Mean?
The apparent failure of individual probabilistic expressions to distinguish uncertainty about truths from uncertainty about probabilistic assessments have prompted researchers to seek formalisms where the two types of uncertainties are given notational distinction.
On the Equivalence of Causal Models
Scientists often use directed acyclic graphs (days) to model the qualitative structure of causal theories, allowing the parameters to be estimated from observational data.
Distributed Revision of Belief Commitment in Multi-Hypothesis Interpretations
This paper extends the applications of belief-networks to include the revision of belief commitments, i. e., the categorical acceptance of a subset of hypotheses which, together, constitute the most satisfactory explanation of the evidence at hand.
Deciding Morality of Graphs is NP-complete
In order to find a causal explanation for data presented in the form of covariance and concentration matrices it is necessary to decide if the graph formed by such associations is a projection of a directed acyclic graph (dag).
Causes and Explanations: A Structural-Model Approach --- Part 1: Causes
We propose a new definition of actual causes, using structural equations to model counterfactuals. We show that the definitions yield a plausible and elegant account ofcausation that handles well examples which have caused problems forother definitions and resolves major difficulties in the traditionalaccount.
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".
On a Class of Bias-Amplifying Variables that Endanger Effect Estimates
This note deals with a class of variables that, if conditioned on, tends to amplify confounding bias in the analysis of causal effects.
On Measurement Bias in Causal Inference
This paper addresses the problem of measurement errors in causal inference and highlights several algebraic and graphical methods for eliminating systematic bias induced by such errors.
