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Found 11 papers, 2 papers with code
Causal Effect Identifiability under Partial-Observability
Finally, building on these graphical properties, we develop an algorithm that returns a formula for a causal effect in terms of the available distributions.
Can We Utilize Pre-trained Language Models within Causal Discovery Algorithms?
Scaling laws have allowed Pre-trained Language Models (PLMs) into the field of causal reasoning.
Causal Identification with Matrix Equations
Causal effect identification is concerned with determining whether a causal effect is computable from a combination of qualitative assumptions about the underlying system (e. g., a causal graph) and distributions collected from this system.
Nested Counterfactual Identification from Arbitrary Surrogate Experiments
In this paper, we study the identification of nested counterfactuals from an arbitrary combination of observations and experiments.
Characterizing Optimal Mixed Policies: Where to Intervene and What to Observe
Intelligent agents are continuously faced with the challenge of optimizing a policy based on what they can observe (see) and which actions they can take (do) in the environment where they are deployed.
Towards Robust Relational Causal Discovery
In practice, queries to a RCI oracle have to be replaced by reliable tests for RCI against available data.
Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality
As virtually all aspects of our lives are increasingly impacted by algorithmic decision making systems, it is incumbent upon us as a society to ensure such systems do not become instruments of unfair discrimination on the basis of gender, race, ethnicity, religion, etc.
Structural Causal Bandits: Where to Intervene?
We study the problem of identifying the best action in a sequential decision-making setting when the reward distributions of the arms exhibit a non-trivial dependence structure, which is governed by the underlying causal model of the domain where the agent is deployed.
Lifted Representation of Relational Causal Models Revisited: Implications for Reasoning and Structure Learning
The correctness of the algorithm proposed by Maier et al. (2013a) for learning RCM from data relies on the soundness and completeness of AGG for relational d-separation to reduce the learning of an RCM to learning of an AGG.
Transportability from Multiple Environments with Limited Experiments
This paper considers the problem of transferring experimental findings learned from multiple heterogeneous domains to a target environment, in which only limited experiments can be performed.
Causal Transportability of Experiments on Controllable Subsets of Variables: z-Transportability
We provide a correct and complete algorithm that determines whether a causal effect is z-transportable; and if it is, produces a transport formula, that is, a recipe for estimating the causal effect of X on Y in the target domain using information elicited from the results of experimental manipulations of Z in the source domain and observational data from the target domain.
