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Methods and tools for causal discovery and causal inference
Causality is a complex concept, which roots its developments across several fields, such as
statistics, economics, epidemiology, computer science, and philosophy. In recent years, the …
statistics, economics, epidemiology, computer science, and philosophy. In recent years, the …
A survey on causal inference
Causal inference is a critical research topic across many domains, such as statistics,
computer science, education, public policy, and economics, for decades. Nowadays …
computer science, education, public policy, and economics, for decades. Nowadays …
Optimal transport for treatment effect estimation
Estimating individual treatment effects from observational data is challenging due to
treatment selection bias. Prevalent methods mainly mitigate this issue by aligning different …
treatment selection bias. Prevalent methods mainly mitigate this issue by aligning different …
Counterfactual vqa: A cause-effect look at language bias
Recent VQA models may tend to rely on language bias as a shortcut and thus fail to
sufficiently learn the multi-modal knowledge from both vision and language. In this paper …
sufficiently learn the multi-modal knowledge from both vision and language. In this paper …
Smartphone app usage analysis: datasets, methods, and applications
As smartphones have become indispensable personal devices, the number of smartphone
users has increased dramatically over the last decade. These personal devices, which are …
users has increased dramatically over the last decade. These personal devices, which are …
From real‐world patient data to individualized treatment effects using machine learning: current and future methods to address underlying challenges
Clinical decision making needs to be supported by evidence that treatments are beneficial to
individual patients. Although randomized control trials (RCTs) are the gold standard for …
individual patients. Although randomized control trials (RCTs) are the gold standard for …
The causal-neural connection: Expressiveness, learnability, and inference
One of the central elements of any causal inference is an object called structural causal
model (SCM), which represents a collection of mechanisms and exogenous sources of …
model (SCM), which represents a collection of mechanisms and exogenous sources of …
Learning causal effects on hypergraphs
Hypergraphs provide an effective abstraction for modeling multi-way group interactions
among nodes, where each hyperedge can connect any number of nodes. Different from …
among nodes, where each hyperedge can connect any number of nodes. Different from …
Learning disentangled representations for counterfactual regression
We consider the challenge of estimating treatment effects from observational data; and point
out that, in general, only some factors based on the observed covariates X contribute to …
out that, in general, only some factors based on the observed covariates X contribute to …
PR-PL: A novel prototypical representation based pairwise learning framework for emotion recognition using EEG signals
Affective brain-computer interface based on electroencephalography (EEG) is an important
branch in the field of affective computing. However, the individual differences in EEG …
branch in the field of affective computing. However, the individual differences in EEG …