# Interpretable (not just posthoc-explainable) heterogeneous survivor bias-corrected treatment effects for assignment of postdischarge interventions to prevent readmissions

arXiv: https://arxiv.org/abs/2304.09981 · HTML: https://mlsanity.com/paper/2304.09981/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 71 references that are in the map.
- First posted on arXiv 19 Apr 2023.

## Builds on (most cited first, 17 of 17)

- [ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission](https://mlsanity.com/paper/1904.05342/) (2019)
- [Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods](https://mlsanity.com/paper/1911.02508/) (2019)
- [Problems with Shapley-value-based explanations as feature importance measures](https://mlsanity.com/paper/2002.11097/) (2020)
- [The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective](https://mlsanity.com/paper/2202.01602/) (2022)
- [TensorFlow Distributions](https://mlsanity.com/paper/1711.10604/) (2017)
- [Do Feature Attribution Methods Correctly Attribute Features?](https://mlsanity.com/paper/2104.14403/) (2021)
- [The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations](https://mlsanity.com/paper/1907.09294/) (2019)
- [Model Selection in Bayesian Neural Networks via Horseshoe Priors](https://mlsanity.com/paper/1705.10388/) (2017)
- [Designing Inherently Interpretable Machine Learning Models](https://mlsanity.com/paper/2111.01743/) (2021)
- [Neural networks versus Logistic regression for 30 days all-cause readmission prediction](https://mlsanity.com/paper/1812.09549/) (2018)
- [DeeprETA: An ETA Post-processing System at Scale](https://mlsanity.com/paper/2206.02127/) (2022)
- [Horseshoe Regularization for Machine Learning in Complex and Deep Models](https://mlsanity.com/paper/1904.10939/) (2019)
- [Predicting Heart Failure Readmission from Clinical Notes Using Deep Learning](https://mlsanity.com/paper/1912.10306/) (2019)
- [Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization](https://mlsanity.com/paper/2012.04171/) (2020)
- [Explainable Health Risk Predictor with Transformer-based Medicare Claim Encoder](https://mlsanity.com/paper/2105.09428/) (2021)
- [Gradient Boosted Decision Tree Neural Network](https://mlsanity.com/paper/1910.09340/) (2019)
- [Asymptotic convergence in distribution of the area bounded by prevalence-weighted Kaplan-Meier curves using empirical process modeling](https://mlsanity.com/paper/1701.02424/) (2017)

## Built on by (most cited first, 1 of 1)

- [A renormalization-group inspired lattice-based framework for piecewise generalized linear models](https://mlsanity.com/paper/2605.05493/) (2026)

## Abstract (arXiv)

We used survival analysis to quantify the impact of postdischarge evaluation and management (E/M) services in preventing hospital readmission or death. Our approach avoids a specific pitfall of applying machine learning to this problem, which is an inflated estimate of the effect of interventions, due to survivors bias -- where the magnitude of inflation may be conditional on heterogeneous confounders in the population. This bias arises simply because in order to receive an intervention after discharge, a person must not have been readmitted in the intervening period. After deriving an expression for this phantom effect, we controlled for this and other biases within an inherently interpretable Bayesian survival framework. We identified case management services as being the most impactful for reducing readmissions overall.