# Nonparametric causal effects based on incremental propensity score interventions

arXiv: https://arxiv.org/abs/1704.00211 · HTML: https://mlsanity.com/paper/1704.00211/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2017.

## Built on by (most cited first, 28 of 28)

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- [Conformal Prediction for Causal Effects of Continuous Treatments](https://mlsanity.com/paper/2407.03094/) (2024)
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- [Efficient Evaluation of Natural Stochastic Policies in Offline Reinforcement Learning](https://mlsanity.com/paper/2006.03886/) (2020)
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- [Positivity-free Policy Learning with Observational Data](https://mlsanity.com/paper/2310.06969/) (2023)
- [Combining Experimental and Historical Data for Policy Evaluation](https://mlsanity.com/paper/2406.00317/) (2024)
- [Hierarchical and Density-based Causal Clustering](https://mlsanity.com/paper/2411.01250/) (2024)
- [Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes](https://mlsanity.com/paper/2404.00099/) (2024)
- [Topological Causal Effects](https://mlsanity.com/paper/2603.02289/) (2026)
- [Relative Sparsity for Medical Decision Problems](https://mlsanity.com/paper/2211.16566/) (2022)
- [Counterfactually Comparing Abstaining Classifiers](https://mlsanity.com/paper/2305.10564/) (2023)
- [Evaluating shifts in mobility and COVID-19 case rates in U.S. counties: A demonstration of modified treatment policies for causal inference with continuous exposures](https://mlsanity.com/paper/2110.12529/) (2021)
- [Semiparametric Inference for Counterfactual Regression under Intervention-Driven Shift](https://mlsanity.com/paper/2504.02694/) (2025)
- [Causal Inference on Networks under Misspecified Exposure Mappings: A Partial Identification Framework](https://mlsanity.com/paper/2602.03459/) (2026)
- [Inference for relative sparsity](https://mlsanity.com/paper/2306.14297/) (2023)
- [Nonparametric estimation of a covariate-adjusted counterfactual treatment regimen response curve](https://mlsanity.com/paper/2309.16099/) (2023)
- [DOLCE: Decomposing Off-Policy Evaluation/Learning into Lagged and Current Effects](https://mlsanity.com/paper/2505.00961/) (2025)
- [The Impact of Medication Non-adherence on Adverse Outcomes: Evidence from Schizophrenia Patients via Survival Analysis](https://mlsanity.com/paper/2506.18187/) (2025)
- [Learning density ratios in causal inference using Bregman-Riesz regression](https://mlsanity.com/paper/2510.16127/) (2025)
- [Nonparametric efficient inference for network quantile causal effects under partial interference](https://mlsanity.com/paper/2604.13008/) (2026)
- [Pessimistic Risk-Aware Policy Learning in Contextual Bandits](https://mlsanity.com/paper/2605.15620/) (2026)
- [Set-Valued Policy Learning](https://mlsanity.com/paper/2605.19830/) (2026)
- [Learning Treatment Effects during Resource Allocation via Priority-Queue Randomization](https://mlsanity.com/paper/2605.25169/) (2026)

## Abstract (arXiv)

Most work in causal inference considers deterministic interventions that set each unit's treatment to some fixed value. However, under positivity violations these interventions can lead to non-identification, inefficiency, and effects with little practical relevance. Further, corresponding effects in longitudinal studies are highly sensitive to the curse of dimensionality, resulting in widespread use of unrealistic parametric models. We propose a novel solution to these problems: incremental interventions that shift propensity score values rather than set treatments to fixed values. Incremental interventions have several crucial advantages. First, they avoid positivity assumptions entirely. Second, they require no parametric assumptions and yet still admit a simple characterization of longitudinal effects, independent of the number of timepoints. For example, they allow longitudinal effects to be visualized with a single curve instead of lists of coefficients. After characterizing these incremental interventions and giving identifying conditions for corresponding effects, we also develop general efficiency theory, propose efficient nonparametric estimators that can attain fast convergence rates even when incorporating flexible machine learning, and propose a bootstrap-based confidence band and simultaneous test of no treatment effect. Finally we explore finite-sample performance via simulation, and apply the methods to study time-varying sociological effects of incarceration on entry into marriage.