Causal Identification
12 papers with code • 0 benchmarks • 1 datasets
Benchmarks
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BCOPA-CE
Latest papers with no code
Algorithmic syntactic causal identification
Our description is given entirely in terms of the non-parametric ADMG structure specifying a causal model and the algebraic signature of the corresponding monoidal category, to which a sequence of manipulations is then applied so as to arrive at a modified monoidal category in which the desired, purely syntactic interventional causal model, is obtained.
Cause and Effect: Can Large Language Models Truly Understand Causality?
The knowledge from ConceptNet enhances the performance of multiple causal reasoning tasks such as causal discovery, causal identification and counterfactual reasoning.
Bridging Methodologies: Angrist and Imbens' Contributions to Causal Identification
Bridging a gap between those two strands of literature, they stress the importance of treatment effect heterogeneity and show that, under defendable assumptions in various applications, this method recovers an average causal effect for a specific subpopulation of individuals whose treatment is affected by the instrument.
Towards Bounding Causal Effects under Markov Equivalence
In this more "data-driven" setting, we provide a systematic algorithm to derive bounds on causal effects that can be computed analytically.
The Blessings of Multiple Treatments and Outcomes in Treatment Effect Estimation
To accommodate these scenarios, we consider a new setting dubbed as multiple treatments and multiple outcomes.
Optimal and Fair Encouragement Policy Evaluation and Learning
While optimal treatment rules can maximize causal outcomes across the population, access parity constraints or other fairness considerations can be relevant in the case of encouragement.
Active and Passive Causal Inference Learning
This paper serves as a starting point for machine learning researchers, engineers and students who are interested in but not yet familiar with causal inference.
Representation Disentaglement via Regularization by Causal Identification
In this work, we propose the use of a causal collider structured model to describe the underlying data generative process assumptions in disentangled representation learning.
Emerging Synergies in Causality and Deep Generative Models: A Survey
In the field of artificial intelligence (AI), the quest to understand and model data-generating processes (DGPs) is of paramount importance.
Causal identification with subjective outcomes
Survey questions often elicit responses on ordered scales for which the definitions of the categories are subjective, possibly varying by individual.
