mlsanityData to 7 Oct 2026

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

arXiv 1706.07840 ↗First posted 23 Jun 2017stat.ME math.ST stat.APcited by 21

Not on the map. Why?

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.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jun 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 3; 2023: 7; 2024: 2; 2025: 3; 2026: 4.

21 papers.

Sort:

  1. 2023High-probability sample complexities for policy evaluation with linear function approximationcited by 16
  2. 2023Correcting for Interference in Experiments: A Case Study at Douyincited by 12
  3. 2026EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environmentscited by 12
  4. 2022Policy Evaluation for Temporal and/or Spatial Dependent Experimentscited by 8
  5. 2023An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Banditscited by 8
  6. 2024Unraveling the Interplay between Carryover Effects and Reward Autocorrelations in Switchback Experimentscited by 6
  7. 2024Combining Experimental and Historical Data for Policy Evaluationcited by 6
  8. 2022Quantile Off-Policy Evaluation via Deep Conditional Generative Learningcited by 5
  9. 2025GST-UNet: A Neural Framework for Spatiotemporal Causal Inference with Time-Varying Confoundingcited by 5
  10. 2022A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided Marketscited by 4
  11. 2021Adaptive Sequential Design for a Single Time-Seriescited by 3
  12. 2025A Two-armed Bandit Framework for A/B Testingcited by 2
9 more
  1. 2023A Unified Representation Framework for Rideshare Marketplace Equilibrium and Efficiencycited by 1
  2. 2026Designing Time Series Experiments in A/B Testing with Transformer Reinforcement Learningcited by 1
  3. 2021Dynamic covariate balancing: estimating treatment effects over time with potential local projectionscited by 0
  4. 2023Balancing Risk and Reward: An Automated Phased Release Strategycited by 0
  5. 2023Causal Estimation of User Learning in Personalized Systemscited by 0
  6. 2023A Meta-Learning Method for Estimation of Causal Excursion Effects to Assess Time-Varying Moderationcited by 0
  7. 2025Unbiased Platform-Level Causal Estimation for Search Systems: A Competitive Isolation PSM-DID Frameworkcited by 0
  8. 2026Fit CATE Once: Model-Assisted Randomization Tests Without Sample Splittingcited by 0
  9. 2026Reconciling Causality and Non-Equilibrium Thermodynamics with Hamiltonian Causal Modelscited by 0