9 papers
Spatial Deconfounder: Interference-Aware Deconfounding for Spatial Causal Inference
Ayush Khot, Miruna Oprescu, Maresa Schröder +2
Causal inference in spatial domains faces two intertwined challenges: (1) unmeasured spatial factors, such as weather, air pollution, or mobility, that confound treatment and outco…
Latent-Space Causal Discovery from Indirect Neuroimaging Observations
Sangyoon Bae, Miruna Oprescu, David Keetae Park +2
Neuroimaging does not observe causal variables directly: hemodynamics and volume conduction distort signals so that statistical dependence need not reflect latent neural influence.…
Causal Inference on Networks under Misspecified Exposure Mappings: A Partial Identification Framework
Maresa Schröder, Miruna Oprescu, Stefan Feuerriegel +1
Estimating treatment effects in networks is challenging, as each potential outcome depends on the treatments of all other nodes in the network. To overcome this difficulty, existin…
Efficient Adaptive Experimentation with Noncompliance
Miruna Oprescu, Brian M Cho, Nathan Kallus
We study the problem of estimating the average treatment effect (ATE) in adaptive experiments where treatment can only be encouraged -- rather than directly assigned -- via a binar…
GST-UNet: A Neural Framework for Spatiotemporal Causal Inference with Time-Varying Confounding
Miruna Oprescu, David K. Park, Xihaier Luo +2
Estimating causal effects from spatiotemporal observational data is essential in public health, environmental science, and policy evaluation, where randomized experiments are often…
Robust and Agnostic Learning of Conditional Distributional Treatment Effects
Nathan Kallus, Miruna Oprescu
The conditional average treatment effect (CATE) is the best measure of individual causal effects given baseline covariates. However, the CATE only captures the (conditional) averag…