# Causal-StoNet: Causal Inference for High-Dimensional Complex Data

arXiv: https://arxiv.org/abs/2403.18994 · HTML: https://mlsanity.com/paper/2403.18994/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 51 references that are in the map.
- First posted on arXiv 27 Mar 2024.

## Builds on (most cited first, 26 of 26)

- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://mlsanity.com/paper/1706.03461/) (2017)
- [Causal Effect Inference with Deep Latent-Variable Models](https://mlsanity.com/paper/1705.08821/) (2017)
- [Stochastic Gradient Hamiltonian Monte Carlo](https://mlsanity.com/paper/1402.4102/) (2014)
- [Adapting Neural Networks for the Estimation of Treatment Effects](https://mlsanity.com/paper/1906.02120/) (2019)
- [Direct and Indirect Effects](https://mlsanity.com/paper/1301.2300/) (2013)
- [Recursive Partitioning for Heterogeneous Causal Effects](https://mlsanity.com/paper/1504.01132/) (2015)
- [Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis](https://mlsanity.com/paper/1702.03849/) (2017)
- [Conformal Inference of Counterfactuals and Individual Treatment Effects](https://mlsanity.com/paper/2006.06138/) (2020)
- [Learning Counterfactual Representations for Estimating Individual Dose-Response Curves](https://mlsanity.com/paper/1902.00981/) (2019)
- [Consistency and fluctuations for stochastic gradient Langevin dynamics](https://mlsanity.com/paper/1409.0578/) (2014)
- [Approximate Residual Balancing: De-Biased Inference of Average Treatment Effects in High Dimensions](https://mlsanity.com/paper/1604.07125/) (2016)
- [Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations](https://mlsanity.com/paper/1309.4686/) (2013)
- [Global Convergence of Stochastic Gradient Hamiltonian Monte Carlo for Non-Convex Stochastic Optimization: Non-Asymptotic Performance Bounds and Momentum-Based Acceleration](https://mlsanity.com/paper/1809.04618/) (2018)
- [Consistent Sparse Deep Learning: Theory and Computation](https://mlsanity.com/paper/2102.13229/) (2021)
- [Estimating Average Treatment Effects via Orthogonal Regularization](https://mlsanity.com/paper/2101.08490/) (2021)
- [An Adaptive Empirical Bayesian Method for Sparse Deep Learning](https://mlsanity.com/paper/1910.10791/) (2019)
- [Nonlinear Sufficient Dimension Reduction with a Stochastic Neural Network](https://mlsanity.com/paper/2210.04349/) (2022)
- [A Kernel-Expanded Stochastic Neural Network](https://mlsanity.com/paper/2201.05319/) (2022)
- [Causal inference with confounders missing not at random](https://mlsanity.com/paper/1702.03951/) (2017)
- [Causal Inference of General Treatment Effects using Neural Networks with A Diverging Number of Confounders](https://mlsanity.com/paper/2009.07055/) (2020)
- [Sparse Deep Learning: A New Framework Immune to Local Traps and Miscalibration](https://mlsanity.com/paper/2110.00653/) (2021)
- [Estimating Causal Effects using a Multi-task Deep Ensemble](https://mlsanity.com/paper/2301.11351/) (2023)
- [Extended Stochastic Gradient MCMC for Large-Scale Bayesian Variable Selection](https://mlsanity.com/paper/2002.02919/) (2020)
- [Causal Decision Trees](https://mlsanity.com/paper/1508.03812/) (2015)
- [Hi-CI: Deep Causal Inference in High Dimensions](https://mlsanity.com/paper/2008.09858/) (2020)
- [High-dimensional confounding adjustment using continuous spike and slab priors](https://mlsanity.com/paper/1704.07532/) (2017)

## Built on by (most cited first, 3 of 3)

- [Mitigating Modality Prior-Induced Hallucinations in Multimodal Large Language Models via Deciphering Attention Causality](https://mlsanity.com/paper/2410.04780/) (2024)
- [Conditional Average Treatment Effect Estimation Under Hidden Confounders](https://mlsanity.com/paper/2506.12304/) (2025)
- [Sublinearly Structured Deep Neural Networks Achieve Feature Learning Consistency for Compositional Functions](https://mlsanity.com/paper/2606.23477/) (2026)

## Abstract (arXiv)

With the advancement of data science, the collection of increasingly complex datasets has become commonplace. In such datasets, the data dimension can be extremely high, and the underlying data generation process can be unknown and highly nonlinear. As a result, the task of making causal inference with high-dimensional complex data has become a fundamental problem in many disciplines, such as medicine, econometrics, and social science. However, the existing methods for causal inference are frequently developed under the assumption that the data dimension is low or that the underlying data generation process is linear or approximately linear. To address these challenges, this paper proposes a novel causal inference approach for dealing with high-dimensional complex data. The proposed approach is based on deep learning techniques, including sparse deep learning theory and stochastic neural networks, that have been developed in recent literature. By using these techniques, the proposed approach can address both the high dimensionality and unknown data generation process in a coherent way. Furthermore, the proposed approach can also be used when missing values are present in the datasets. Extensive numerical studies indicate that the proposed approach outperforms existing ones.