ICLR 2025

Published as a conference paper at ICLR 2025
ROBUST SIMULATION-BASED INFERENCE
UNDER MISSING DATA VIA NEURAL PROCESSES
Yogesh Verma, Ayush Bharti
Department of Computer Science, Aalto University
{yogesh.verma, ayush.bharti}@aalto.fi
Vikas Garg
YaiYai Ltd and Aalto University
vgarg@csail.mit.edu
ABSTRACT
Simulation-based inference (SBI) methods typically require fully observed data to
infer parameters of models with intractable likelihood functions. However, datasets
often contain missing values due to incomplete observations, data corruptions
(common in astrophysics), or instrument limitations (e.g., in high-energy physics
applications). In such scenarios, missing data must be imputed before applying any
SBI method. We formalize the problem of missing data in SBI and demonstrate that
naive imputation methods can introduce bias in the estimation of SBI posterior. We
also introduce a novel amortized method that addresses this issue by jointly learning
the imputation model and the inference network within a neural posterior estimation
(NPE) framework. Extensive empirical results on SBI benchmarks show that our
approach provides robust inference outcomes compared to standard baselines
for varying levels of missing data. Moreover, we demonstrate the merits of our
imputation model on two real-world bioactivity datasets (Adrenergic and Kinase
assays). Code is available at https://github.com/Aalto-QuML/RISE.
1
INTRODUCTION
Mechanistic models for studying complex physical or biological phenomena have become indispens-
able tools in research fields as diverse as genetics (Riesselman et al., 2018), epidemiology (Kypraios
et al., 2017), gravitational wave astronomy (Dax et al., 2021), and radio propagation (Bharti et al.,
2022a). However, fitting such models to observational data can be challenging due to the intractabil-
ity of their likelihood functions, which renders standard Bayesian inference methods inapplicable.
Simulation-based inference (SBI) methods (Cranmer et al., 2...