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Related Concept Videos

Counterfactual Thinking01:19

Counterfactual Thinking

Counterfactual thinking is a cognitive process wherein individuals mentally reconstruct alternative versions of past events, often beginning with “what if” or “if only.” This reflective mechanism plays a significant role in shaping emotional experiences and guiding future behavior. Though typically triggered by unfavorable or unexpected outcomes, counterfactual thinking can also emerge in mundane, everyday decisions and experiences, revealing its deep entrenchment in human cognition.Types of...
Cognitive Learning01:21

Cognitive Learning

Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
Constraints and Statical Determinacy01:26

Constraints and Statical Determinacy

In structural engineering, the equilibrium of a system is not only determined by its equations of equilibrium but also with the help of constraints. Constraints refer to restrictions on the motion of a system. The proper combinations of constraints can minimize the total number of constraints needed to maintain a system in mechanical equilibrium. When this happens, the system is said to be statically determinate. For such systems, the unknown reaction supports can be estimated using equilibrium...
Deductive Reasoning01:16

Deductive Reasoning

Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
For example, a researcher can deduce specific predictions...
Purposive Learning01:22

Purposive Learning

E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a bonus...
Inductive Reasoning00:59

Inductive Reasoning

Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...

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Related Experiment Videos

Causal Structure Learning Assumptions Shape Counterfactual Safety: Expert-Guided Constraints vs. Data-Driven DAGs

Héctor Avilés1, Ingridh Gracia1, Rafael Kiesel2

  • 1Department of Information Technologies, Polytechnic University of Victoria, Ciudad Victoria 87138, Mexico.

Entropy (Basel, Switzerland)
|May 26, 2026
PubMed
Summary

Causal DAG learning algorithms significantly impact counterfactual decision safety. Structure learning choices, especially expert guidance, are crucial for identifying safe actions in critical applications.

Keywords:
autonomous systemscausal discoverycounterfactual reasoningprobabilistic logic

Related Experiment Videos

Area of Science:

  • Causal inference
  • Machine learning
  • Autonomous systems

Background:

  • Causal discovery algorithms are essential for understanding complex systems.
  • Ensuring safety in autonomous systems requires reliable counterfactual decision-making.
  • The influence of structural assumptions in causal learning on decision safety is not fully understood.

Purpose of the Study:

  • To investigate how different causal Directed Acyclic Graph (DAG) learning algorithms and structural assumptions affect counterfactual decision safety.
  • To compare the performance of four distinct structure learning regimes in a controlled autonomous driving environment.
  • To analyze the impact of these learning strategies on the identification of safe actions.

Main Methods:

  • Four causal structure learning algorithms were compared: expert-guided edge-constrained HC+BIC, unconstrained HC+BIC, MMPC+HC+BIC, and PC-Stable.
  • Evaluation used a leave-one-state-out protocol in a simulated autonomous driving setting with seven Boolean state variables and six actions.
  • All models were implemented as probabilistic logic twin networks (PLTNs), with sensitivity analysis performed on parameter configurations.

Main Results:

  • Different learning regimes produced markedly different counterfactual decisions.
  • Edge-constrained HC+BIC recommended a diverse set of safe actions.
  • Unconstrained HC+BIC identified fewer but consistently safe actions; MMPC+HC+BIC often failed to identify safe actions; PC-Stable sometimes included unsafe actions due to incorrect edge orientations.

Conclusions:

  • Structure learning choices and prior knowledge significantly influence counterfactual decisions through the learned causal structure.
  • These choices directly affect the ability to identify safe alternatives in safety-critical applications.
  • Careful consideration of causal learning algorithms and structural assumptions is vital for developing safe autonomous systems.