# Time series experiments and causal estimands: exact randomization tests and trading

arXiv: https://arxiv.org/abs/1706.07840 · HTML: https://mlsanity.com/paper/1706.07840/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2017.

## Built on by (most cited first, 21 of 21)

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- [Unraveling the Interplay between Carryover Effects and Reward Autocorrelations in Switchback Experiments](https://mlsanity.com/paper/2403.17285/) (2024)
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- [Quantile Off-Policy Evaluation via Deep Conditional Generative Learning](https://mlsanity.com/paper/2212.14466/) (2022)
- [GST-UNet: A Neural Framework for Spatiotemporal Causal Inference with Time-Varying Confounding](https://mlsanity.com/paper/2502.05295/) (2025)
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- [Designing Time Series Experiments in A/B Testing with Transformer Reinforcement Learning](https://mlsanity.com/paper/2602.01853/) (2026)
- [Dynamic covariate balancing: estimating treatment effects over time with potential local projections](https://mlsanity.com/paper/2103.01280/) (2021)
- [Balancing Risk and Reward: An Automated Phased Release Strategy](https://mlsanity.com/paper/2305.09626/) (2023)
- [Causal Estimation of User Learning in Personalized Systems](https://mlsanity.com/paper/2306.00485/) (2023)
- [A Meta-Learning Method for Estimation of Causal Excursion Effects to Assess Time-Varying Moderation](https://mlsanity.com/paper/2306.16297/) (2023)
- [Unbiased Platform-Level Causal Estimation for Search Systems: A Competitive Isolation PSM-DID Framework](https://mlsanity.com/paper/2511.01329/) (2025)
- [Fit CATE Once: Model-Assisted Randomization Tests Without Sample Splitting](https://mlsanity.com/paper/2605.09116/) (2026)
- [Reconciling Causality and Non-Equilibrium Thermodynamics with Hamiltonian Causal Models](https://mlsanity.com/paper/2606.04822/) (2026)

## Abstract (arXiv)

We define causal estimands for experiments on single time series, extending the potential outcome framework to dealing with temporal data. Our approach allows the estimation of some of these estimands and exact randomization based p-values for testing causal effects, without imposing stringent assumptions. We test our methodology on simulated "potential autoregressions,"which have a causal interpretation. Our methodology is partially inspired by data from a large number of experiments carried out by a financial company who compared the impact of two different ways of trading equity futures contracts. We use our methodology to make causal statements about their trading methods.