Joint Point Process Model for Counterfactual Treatment–Outcome Trajectories Under Policy Interventions

Caglar Hizli*, Ti John, Anne Juuti, Tuure Saarinen, Kirsi Pietiläinen, Pekka Marttinen

*Corresponding author for this work

Research output: Contribution to conferencePaperScientificpeer-review


Policy makers need to predict the progression of an outcome before adopting
a new treatment policy, which defines when and how a sequence of treatments
affecting the outcome occurs in continuous time. Commonly, algorithms that
predict interventional future outcome trajectories take a fixed sequence of future treatments as input. This excludes scenarios where the policy is unknown or a counterfactual analysis is needed. To handle these limitations, we develop a joint model for treatments and outcomes, which allows for the estimation of treatment policies and effects from sequential treatment–outcome data. It can answer interventional and counterfactual queries about interventions on treatment policies, as we show with a realistic semi-synthetic simulation study. This abstract is based on work that is currently under review for AAAI-23.
Original languageEnglish
Publication statusPublished - 2022
MoE publication typeNot Eligible
EventConference on Neural Information Processing Systems - New Orleans, United States
Duration: 28 Nov 20229 Dec 2022
Conference number: 36


ConferenceConference on Neural Information Processing Systems
Abbreviated titleNeurIPS
Country/TerritoryUnited States
CityNew Orleans
Internet address


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