Search Results for author:
Found 22 papers, 2 papers with code
Robust COVID-19 Detection in CT Images with CLIP
In the realm of medical imaging, particularly for COVID-19 detection, deep learning models face substantial challenges such as the necessity for extensive computational resources, the paucity of well-annotated datasets, and a significant amount of unlabeled data.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
