Counterfactual Reasoning
83 papers with code • 0 benchmarks • 2 datasets
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Most implemented papers
Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning
We propose a unified mechanism for achieving coordination and communication in Multi-Agent Reinforcement Learning (MARL), through rewarding agents for having causal influence over other agents' actions.
Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning
First, we propose reward machines, a type of finite state machine that supports the specification of reward functions while exposing reward function structure.
Explaining by Removing: A Unified Framework for Model Explanation
We describe a new unified class of methods, removal-based explanations, that are based on the principle of simulating feature removal to quantify each feature's influence.
Counterfactual Phenotyping with Censored Time-to-Events
Estimation of treatment efficacy of real-world clinical interventions involves working with continuous outcomes such as time-to-death, re-hospitalization, or a composite event that may be subject to censoring.
Transformers as Soft Reasoners over Language
However, expressing the knowledge in a formal (logical or probabilistic) representation has been a major obstacle to this research.
Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect
On one hand, it has a harmful causal effect that misleads the tail prediction biased towards the head.
Counterfactual Explainable Recommendation
Technically, for each item recommended to each user, CountER formulates a joint optimization problem to generate minimal changes on the item aspects so as to create a counterfactual item, such that the recommendation decision on the counterfactual item is reversed.
COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics
Many applications of text generation require incorporating different constraints to control the semantics or style of generated text.
Reasoning about Counterfactuals to Improve Human Inverse Reinforcement Learning
We propose to incorporate the learner's current understanding of the robot's decision making into our model of human IRL, so that a robot can select demonstrations that maximize the human's understanding.
GCAN: Generative Counterfactual Attention-guided Network for Explainable Cognitive Decline Diagnostics based on fMRI Functional Connectivity
Furthermore, to tackle the difficulty in the generation of highly-structured and brain-atlas-constrained FC, which is essential in counterfactual reasoning, an Atlas-Aware Bidirectional Transformer (AABT) method is developed.