Counterfactual Inference
49 papers with code • 0 benchmarks • 2 datasets
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Latest papers with no code
Identification of Causal Relationship between Amyloid-beta Accumulation and Alzheimer's Disease Progression via Counterfactual Inference
Alzheimer's disease (AD) is a neurodegenerative disorder that is beginning with amyloidosis, followed by neuronal loss and deterioration in structure, function, and cognition.
Causal Inference via Predictive Coding
Bayesian inference models observations: what can be inferred about y if we observe a related variable x?
Self-Interpretable Time Series Prediction with Counterfactual Explanations
Interpretable time series prediction is crucial for safety-critical areas such as healthcare and autonomous driving.
Advancing Counterfactual Inference through Nonlinear Quantile Regression
Traditional counterfactual inference, under Pearls' counterfactual framework, typically depends on having access to or estimating a structural causal model.
Unveiling Cross Modality Bias in Visual Question Answering: A Causal View with Possible Worlds VQA
In this paper, we first model a confounding effect that causes language and vision bias simultaneously, then propose a counterfactual inference to remove the influence of this effect.
Meta-causal Learning for Single Domain Generalization
Under this paradigm, we propose a meta-causal learning method to learn meta-knowledge, that is, how to infer the causes of domain shift between the auxiliary and source domains during training.
Counterfactual (Non-)identifiability of Learned Structural Causal Models
The size of this error can be an essential metric for deciding whether or not DSCMs are a viable approach for counterfactual inference in a specific problem setting.
Debiasing Stance Detection Models with Counterfactual Reasoning and Adversarial Bias Learning
Stance detection models may tend to rely on dataset bias in the text part as a shortcut and thus fail to sufficiently learn the interaction between the targets and texts.
Doubly robust nearest neighbors in factor models
We consider a matrix completion problem with missing data, where the $(i, t)$-th entry, when observed, is given by its mean $f(u_i, v_t)$ plus mean-zero noise for an unknown function $f$ and latent factors $u_i$ and $v_t$.
On counterfactual inference with unobserved confounding
Given an observational study with $n$ independent but heterogeneous units, our goal is to learn the counterfactual distribution for each unit using only one $p$-dimensional sample per unit containing covariates, interventions, and outcomes.
