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Found 7 papers, 2 papers with code
Causal Effect Inference with Deep Latent-Variable Models
Learning individual-level causal effects from observational data, such as inferring the most effective medication for a specific patient, is a problem of growing importance for policy makers.
Ranked #9 on
Causal Inference
on IHDP
From Ordinary Differential Equations to Structural Causal Models: the deterministic case
We show how, and under which conditions, the equilibrium states of a first-order Ordinary Differential Equation (ODE) system can be described with a deterministic Structural Causal Model (SCM).
Cyclic Causal Discovery from Continuous Equilibrium Data
We propose a method for learning cyclic causal models from a combination of observational and interventional equilibrium data.
Learning Sparse Causal Models is not NP-hard
This paper shows that causal model discovery is not an NP-hard problem, in the sense that for sparse graphs bounded by node degree k the sound and complete causal model can be obtained in worst case order N^{2(k+2)} independence tests, even when latent variables and selection bias may be present.
Causal Discovery with Continuous Additive Noise Models
We consider the problem of learning causal directed acyclic graphs from an observational joint distribution.
On Causal and Anticausal Learning
We consider the problem of function estimation in the case where an underlying causal model can be inferred.
Inferring deterministic causal relations
We consider two variables that are related to each other by an invertible function.
