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Found 6 papers, 4 papers with code
Substitute adjustment via recovery of latent variables
Our results support that when the latent variable model of the regressors hold, substitute adjustment is a viable method for adjusted regression.
Efficient adjustment for complex covariates: Gaining efficiency with DOPE
Based on our theoretical results, we propose the Debiased Outcome-adapted Propensity Estimator (DOPE) for efficient estimation of the ATE, and we provide asymptotic results for the DOPE under general conditions.
Nonparametric Conditional Local Independence Testing
It describes whether the evolution of one process is directly influenced by another process given the histories of additional processes, and it is important for the description and learning of causal relations among processes.
Local Independence Testing for Point Processes
Existing tests require strong model assumptions, e. g. that the true data generating model is a Hawkes process with no latent confounders.
Graphical continuous Lyapunov models
The linear Lyapunov equation of a covariance matrix parametrizes the equilibrium covariance matrix of a stochastic process.
Penalized estimation in large-scale generalized linear array models
Computation and storage of its tensor product design matrix can be impossible due to time and memory constraints, and previously considered design matrix free algorithms do not scale well with the dimension of the parameter vector.
