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Found 42 papers, 6 papers with code
Instrumental Variable Estimation for Causal Inference in Longitudinal Data with Time-Dependent Latent Confounders
Causal inference from longitudinal observational data is a challenging problem due to the difficulty in correctly identifying the time-dependent confounders, especially in the presence of latent time-dependent confounders.
Disentangled Latent Representation Learning for Tackling the Confounding M-Bias Problem in Causal Inference
To address this problem with co-occurring M-bias and confounding bias, we propose a novel Disentangled Latent Representation learning framework for learning latent representations from proxy variables for unbiased Causal effect Estimation (DLRCE) from observational data.
Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencoder
In this paper, we relax some of the restrictions by proposing the concept of conditional front-door (CFD) adjustment and develop the theorem that guarantees the causal effect identifiability of CFD adjustment.
Conditional Instrumental Variable Regression with Representation Learning for Causal Inference
To address these challenging and practical problems of the standard IV method (linearity assumption and the strict condition), in this paper, we use a conditional IV (CIV) to relax the unconfounded instrument condition of standard IV and propose a non-linear CIV regression with Confounding Balancing Representation Learning, CBRL. CIV, for jointly eliminating the confounding bias from unobserved confounders and balancing the observed confounders, without the linearity assumption.
Learning Conditional Instrumental Variable Representation for Causal Effect Estimation
One of the fundamental challenges in causal inference is to estimate the causal effect of a treatment on its outcome of interest from observational data.
Causal Effect Estimation with Variational AutoEncoder and the Front Door Criterion
In practice, it is often difficult to identify the set of variables used for front-door adjustment from data.
Linking a predictive model to causal effect estimation
This paper first tackles the challenge of estimating the causal effect of any feature (as the treatment) on the outcome w. r. t.
Disentangled Representation for Causal Mediation Analysis
Causal mediation analysis is a method that is often used to reveal direct and indirect effects.
Causal Inference with Conditional Instruments using Deep Generative Models
The instrumental variable (IV) approach is a widely used way to estimate the causal effects of a treatment on an outcome of interest from observational data with latent confounders.
Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Survey
In recent years, research has emerged to use search strategies based on graphical causal modelling to discover useful knowledge from data for causal effect estimation, with some mild assumptions, and has shown promise in tackling the practical challenge.
Disentangled Representation with Causal Constraints for Counterfactual Fairness
Much research has been devoted to the problem of learning fair representations; however, they do not explicitly the relationship between latent representations.
Explanatory causal effects for model agnostic explanations
This paper studies the problem of estimating the contributions of features to the prediction of a specific instance by a machine learning model and the overall contribution of a feature to the model.
Discovering Ancestral Instrumental Variables for Causal Inference from Observational Data
Based on the theory, we propose a data-driven algorithm to discover a pair of IVs from data.
Ancestral Instrument Method for Causal Inference without Complete Knowledge
Based on the theory, we develop an algorithm for unbiased causal effect estimation with a given ancestral IV and observational data.
Any Part of Bayesian Network Structure Learning
We study an interesting and challenging problem, learning any part of a Bayesian network (BN) structure.
Causal Learner: A Toolbox for Causal Structure and Markov Blanket Learning
Causal Learner is a toolbox for learning causal structure and Markov blanket (MB) from data.
Dependency-based Anomaly Detection: a General Framework and Comprehensive Evaluation
Anomaly detection is crucial for understanding unusual behaviors in data, as anomalies offer valuable insights.
Learning causal representations for robust domain adaptation
In this paper, we study the cases where at the training phase the target domain data is unavailable and only well-labeled source domain data is available, called robust domain adaptation.
Assessing Classifier Fairness with Collider Bias
The increasing application of machine learning techniques in everyday decision-making processes has brought concerns about the fairness of algorithmic decision-making.
Sufficient Dimension Reduction for Average Causal Effect Estimation
Having a large number of covariates can have a negative impact on the quality of causal effect estimation since confounding adjustment becomes unreliable when the number of covariates is large relative to the samples available.
A unified survey of treatment effect heterogeneity modeling and uplift modeling
A central question in many fields of scientific research is to determine how an outcome would be affected by an action, or to measure the effect of an action (a. k. a treatment effect).
Computational methods for cancer driver discovery: A survey
Due to the complexity of the mechanistic insight of cancer genes in driving cancer and the fast development of the field, it is necessary to have a comprehensive review about the current computational methods for discovering different types of cancer drivers.
A general framework for causal classification
We also propose a general framework for causal classification, by using off-the-shelf supervised methods for flexible implementations.
Towards unique and unbiased causal effect estimation from data with hidden variables
Based on the theorems, two algorithms are proposed for finding the proper adjustment sets from data with hidden variables to obtain unbiased and unique causal effect estimation.
Treatment effect estimation with disentangled latent factors
Much research has been devoted to the problem of estimating treatment effects from observational data; however, most methods assume that the observed variables only contain confounders, i. e., variables that affect both the treatment and the outcome.
Causal query in observational data with hidden variables
In this paper, we develop a theorem for using local search to find a superset of the adjustment (or confounding) variables for causal effect estimation from observational data under a realistic pretreatment assumption.
Causality-based Feature Selection: Methods and Evaluations
It has been shown that the knowledge about the causal relationships between features and the class variable has potential benefits for building interpretable and robust prediction models, since causal relationships imply the underlying mechanism of a system.
Linking Graph Entities with Multiplicity and Provenance
The graph model is versatile, thus, it is capable of handling multiple values for an attribute or a relationship, as well as the provenance descriptions of the values.
Identify treatment effect patterns for personalised decisions
The existing interpretable modelling methods take a top-down approach to search for subgroups with heterogeneous treatment effects and they may miss the most specific and relevant context for an individual.
Robust Multi-instance Learning with Stable Instances
Multi-instance learning (MIL) deals with tasks where data is represented by a set of bags and each bag is described by a set of instances.
An exploration of algorithmic discrimination in data and classification
The paper uses real world data sets to demonstrate the existence of discrimination and the independence between the discrimination of data sets and the discrimination of classification models.
FairMod - Making Predictive Models Discrimination Aware
It models the post-processing of predictions problem as a nonlinear optimization problem to find best adjustments to the predictions so that the discrimination constraints of all protected variables are all met at the same time.
Discovering Context Specific Causal Relationships
With the increasing need of personalised decision making, such as personalised medicine and online recommendations, a growing attention has been paid to the discovery of the context and heterogeneity of causal relationships.
A Unified View of Causal and Non-causal Feature Selection
The unified view will fill in the gap in the research of the relation between the two types of methods.
Discovering Markov Blanket from Multiple interventional Datasets
In this paper, we study the problem of discovering the Markov blanket (MB) of a target variable from multiple interventional datasets.
Building Diversified Multiple Trees for Classification in High Dimensional Noisy Biomedical Data
It is common that a trained classification model is applied to the operating data that is deviated from the training data because of noise.
A Review on Algorithms for Constraint-based Causal Discovery
Recent years, as the availability of abundant large-sized and complex observational data, the constrain-based approaches have gradually attracted a lot of interest and have been widely applied to many diverse real-world problems due to the fast running speed and easy generalizing to the problem of causal insufficiency.
ParallelPC: an R package for efficient constraint based causal exploration
Discovering causal relationships from data is the ultimate goal of many research areas.
Mining Combined Causes in Large Data Sets
A straightforward approach to uncovering a combined cause is to include both individual and combined variables in the causal discovery using existing methods, but this scheme is computationally infeasible due to the huge number of combined variables.
Causal Decision Trees
Classification methods are fast and they could be practical substitutes for finding causal signals in data.
From Observational Studies to Causal Rule Mining
Specifically, association rule mining can be used to deal with the high-dimensionality problem while observational studies can be utilised to eliminate non-causal associations.
A fast PC algorithm for high dimensional causal discovery with multi-core PCs
However, runtime of the PC algorithm, in the worst-case, is exponential to the number of nodes (variables), and thus it is inefficient when being applied to high dimensional data, e. g. gene expression datasets.
