14 papers · 1 filter
Interventional Processes for Causal Uncertainty Quantification
Hugh Dance, Peter Orbanz, Arthur Gretton
Reliable uncertainty quantification for causal effects is crucial in high-stakes applications, but remains challenging when the target is an entire function rather than a scalar es…
Outcome-Aware Spectral Feature Learning for Instrumental Variable Regression
Dimitri Meunier, Jakub Wornbard, Vladimir R. Kostic +5
We address the problem of causal effect estimation in the presence of hidden confounders using nonparametric instrumental variable (IV) regression. An established approach is to us…
Semiparametrically Efficient Inference for Kernel Measures of Noise Heterogeneity
Jakub Wornbard, Zikai Shen, Dimitri Meunier +1
We develop semiparametrically efficient inference for kernel measures of noise heterogeneity in additive noise models. In many applications, the regression function is estimated us…
Nonparametric Instrumental Variable Analysis Without Structural Equations: Debiased Inference on Functionals of Inverse Problems with No Solutions
Zikai Shen, Nathan Kallus, Dimitri Meunier +3
We consider debiased inference on finite-dimensional functionals of infinite-dimensional least-squares solutions to inverse problems as a way to avoid having to assume exact soluti…
Semiparametric Efficient Test for Interpretable Distributional Treatment Effects
Houssam Zenati, Arthur Gretton
Distributional treatment effects can be invisible to means: a treatment may preserve average outcomes while changing tails, modes, dispersion, or rare-event probabilities. Kernel t…
Kernel Treatment Effects with Adaptively Collected Data
Houssam Zenati, Bariscan Bozkurt, Arthur Gretton
Adaptive experiments improve efficiency by adjusting treatment assignments based on past outcomes, but this adaptivity breaks the i.i.d.\ assumptions that underpin classical asympt…