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Found 3 papers, 1 papers with code
On the Parameter Identifiability of Partially Observed Linear Causal Models
Linear causal models are important tools for modeling causal dependencies and yet in practice, only a subset of the variables can be observed.
On the Three Demons in Causality in Finance: Time Resolution, Nonstationarity, and Latent Factors
Financial data is generally time series in essence and thus suffers from three fundamental issues: the mismatch in time resolution, the time-varying property of the distribution - nonstationarity, and causal factors that are important but unknown/unobserved.
A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables
Most existing causal discovery methods rely on the assumption of no latent confounders, limiting their applicability in solving real-life problems.
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