# C-kNN-LSH: A Nearest-Neighbor Algorithm for Sequential Counterfactual Inference

arXiv: https://arxiv.org/abs/2602.02371 · HTML: https://mlsanity.com/paper/2602.02371/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 2 Feb 2026.

## Builds on (most cited first, 10 of 10)

- [Causal Effect Inference with Deep Latent-Variable Models](https://mlsanity.com/paper/1705.08821/) (2017)
- [Counterfactual inference in sequential experiments](https://mlsanity.com/paper/2202.06891/) (2022)
- [Sample-Optimal PAC Learning of Halfspaces with Malicious Noise](https://mlsanity.com/paper/2102.06247/) (2021)
- [A Large-Language Model Framework for Relative Timeline Extraction from PubMed Case Reports](https://mlsanity.com/paper/2504.12350/) (2025)
- [Universal Neural Optimal Transport](https://mlsanity.com/paper/2212.00133/) (2022)
- [Causal Flow-based Variational Auto-Encoder for Disentangled Causal Representation Learning](https://mlsanity.com/paper/2304.09010/) (2023)
- [Metric-Fair Prompting: Treating Similar Samples Similarly](https://mlsanity.com/paper/2512.07608/) (2025)
- [Active Learning for Forecasting Severity among Patients with Post Acute Sequelae of SARS-CoV-2](https://mlsanity.com/paper/2506.22444/) (2025)
- [Probabilistic Modelling is Sufficient for Causal Inference](https://mlsanity.com/paper/2512.23408/) (2025)
- [Immunological Density Shapes Recovery Trajectories in Long COVID](https://mlsanity.com/paper/2601.07854/) (2026)

## Abstract (arXiv)

Estimating causal effects from longitudinal trajectories is central to understanding the progression of complex conditions and optimizing clinical decision-making, such as comorbidities and long COVID recovery. We introduce C-kNN--LSH, a nearest-neighbor framework for sequential causal inference designed to handle such high-dimensional, confounded situations. By utilizing locality-sensitive hashing, we efficiently identify ``clinical twins'' with similar covariate histories, enabling local estimation of conditional treatment effects across evolving disease states. To mitigate bias from irregular sampling and shifting patient recovery profiles, we integrate neighborhood estimator with a doubly-robust correction. Theoretical analysis guarantees our estimator is consistent and second-order robust to nuisance error. Evaluated on a real-world Long COVID cohort with 13,511 participants, C-kNN-LSH demonstrates superior performance in capturing recovery heterogeneity and estimating policy values compared to existing baselines.