On causal inference with marked point process data

TL;DR

This paper develops a causal inference framework for data modeled by marked point processes, introducing new conditions and formulas that unify survival analysis and discrete-time causal inference.

Contribution

It formulates new causal inference conditions for marked point process data using martingale theory, bridging survival analysis and discrete-time causal methods.

Findings

Defined dynamic treatment regimes for MPP data.

Derived explicit identifying assumptions based on martingale theory.

Characterized marginal g-formulas for causal effects in MPPs.

Abstract

We define dynamic treatment regimes and associated potential outcomes for data described by marked point processes (MPPs). These definitions motivate MPP analogues of the commonly used consistency, exchangeability, and positivity conditions that are sufficient for identifying effects in MPP data structures. The conditions are formulated based on martingale theory, which allows us to derive explicit identifying assumptions for data described by stochastic processes. The definitions and conditions align with well-established discrete-time results in important special cases. Thus, this work bridges the large literatures on survival (event history) analysis with counting processes in continuous time and causal inference with variables in discrete-time. After formulating a set of identification conditions, we derive and characterize marginal g-formulas. The g-formulas are generally different from those studied in related works, though they coincide in important special cases. We relate our findings to previous work on causal inference with (counting) processes, the classical survival literature, and the discrete-time causal inference literature.