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

## TL;DR

This paper extends causal inference methods to single time series experiments, enabling exact testing of causal effects without strict assumptions, with applications in finance trading strategies.

## Contribution

It introduces a framework for defining and estimating causal effects in time series experiments using potential outcomes and randomization tests, applicable to real-world financial data.

## Key findings

- Successfully tested on simulated autoregressions with causal interpretation
- Provided causal insights into trading strategies used by a financial firm
- Demonstrated the applicability of exact randomization tests in temporal data

## Abstract

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.