Counterfactual Inference
49 papers with code • 0 benchmarks • 2 datasets
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Most implemented papers
Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems
kaushalpaneri/ode2scm
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NeurIPS 2019
In contrast, structural causal models support counterfactual inference, but do not identify the mechanisms.
Counterfactual VQA: A Cause-Effect Look at Language Bias
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.
Learning Decomposed Representation for Counterfactual Inference
anpwu/der-cfr
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The fundamental problem in treatment effect estimation from observational data is confounder identification and balancing.
Enabling Counterfactual Survival Analysis with Balanced Representations
paidamoyo/counterfactual_survival_analysis
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Balanced representation learning methods have been applied successfully to counterfactual inference from observational data.
SemEval-2020 Task 5: Counterfactual Recognition
lenyabloko/SemEval2020
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SEMEVAL 2020
Subtask-1 aims to determine whether a given sentence is a counterfactual statement or not.
Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue
However, we argue that there is a significant gap between clicks and user satisfaction -- it is common that a user is "cheated" to click an item by the attractive title/cover of the item.
Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System
weitianxin/MACR
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Existing work addresses this issue with Inverse Propensity Weighting (IPW), which decreases the impact of popular items on the training and increases the impact of long-tail items.
Causal Expectation-Maximisation
Structural causal models are the basic modelling unit in Pearl's causal theory; in principle they allow us to solve counterfactuals, which are at the top rung of the ladder of causation.
Leveraging Structured Biological Knowledge for Counterfactual Inference: a Case Study of Viral Pathogenesis
bel2scm/bel2scm
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This manuscript proposes a general approach for querying a causal biological knowledge graph, and converting the qualitative result into a quantitative structural causal model that can learn from data to answer the question.
A Structural Causal Model for MR Images of Multiple Sclerosis
jcreinhold/counterfactualms
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Precision medicine involves answering counterfactual questions such as "Would this patient respond better to treatment A or treatment B?"
