Year: 2023
Type: book-chapter
Abstract: In this chapter, the authors review the class of causal effects based on incremental propensity score interventions proposed. The aim of incremental propensity score interventions is to estimate the effect of increasing or decreasing subjects' odds of receiving treatment; this differs from the average treatment effect, where the aim is to estimate the effect of everyone deterministically receiving versus not receiving treatment. Dynamic interventions allow the intervention to depend on subjects' covariate information; thus, the intervention is allowed to vary across subjects, depending on their covariate values, albeit still in a deterministic way. A stochastic intervention assigns treatment randomly based on a probability distribution. Stochastic interventions are perhaps closest to what can be realistically implemented in many applications. In time-varying causal inference, or generally when the treatment can take many values, a tension arises between the number of possible interventions and how well the people can estimate effects at each intervention.
Source: Chapman & Hall/CRC eBooks
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3207808507