# Quantifying and Detecting Individual Level `Always Survivor' Causal Effects Under `Truncation by Death' and Censoring Through Time

## TL;DR

This paper introduces a method to identify individual causal effects in the 'always survivor' group under death truncation, using only assumptions guaranteed by randomized trial design, demonstrated with prostate cancer data.

## Contribution

It provides the first methodology to detect causal effects under death truncation with minimal assumptions, applicable to all outcome types.

## Key findings

- Method successfully identifies individual causal effects in prostate cancer trial.
- Applicable to all outcome types with minimal assumptions.
- First approach to detect effects under death truncation using only trial design assumptions.

## Abstract

The analysis of causal effects when the outcome of interest is possibly truncated by death has a long history in statistics and causal inference. The survivor average causal effect is commonly identified with more assumptions than those guaranteed by the design of a randomized clinical trial or using sensitivity analysis. This paper demonstrates that individual level causal effects in the `always survivor' principal stratum can be identified with no stronger identification assumptions than randomization. We illustrate the practical utility of our methods using data from a clinical trial on patients with prostate cancer. Our methodology is the first and, as of yet, only proposed procedure that enables detecting causal effects in the presence of truncation by death using only the assumptions that are guaranteed by design of the clinical trial. This methodology is applicable to all types of outcomes.