In many fields of scientific research and real-world applications, unbiased estimation of
causal effects from non-experimental data is crucial for understanding the mechanism …

Causality lays the foundation for the trajectory of our world. Causal inference (CI), which
aims to infer intrinsic causal relations among variables of interest, has emerged as a crucial …

Inferring causal structure poses a combinatorial search problem that typically involves
evaluating structures with a score or independence test. The resulting search is costly, and …

We address the problem of causal effect estima-tion in the presence of unobserved
confounding, but where proxies for the latent confounder (s) areobserved. We propose two …

We develop an approach for estimating models described via conditional moment
restrictions, with a prototypical application being non-parametric instrumental variable …

Causal inference from observational data is crucial for many disciplines such as medicine
and economics. However, sharp bounds for causal effects under relaxations of the …

We study the estimation of causal parameters when not all confounders are observed and
instead negative controls are available. Recent work has shown how these can enable …

Instrumental variable (IV) regression is a standard strategy for learning causal relationships
between confounded treatment and outcome variables from observational data by utilizing …

We present a novel algorithm for non-linear instrumental variable (IV) regression, DualIV,
which simplifies traditional two-stage methods via a dual formulation. Inspired by problems …

Many scientific datasets are compositional in nature. Important biological examples include
species abundances in ecology, cell-type compositions derived from single-cell sequencing …