This paper presents an approach to trigger runtime interventions at runtime, in order to improve the success rate of a process, when the number of resources who can perform these interventions is limited. The paper is available at: https://link.springer.com/chapter/10.1007/978-3-031-16171-1_13 The presentation delivered at the 20th International Conference on Business Process Management (BPM'2022), in Muenster, Germany, September 2022.

1. When to intervene?
Prescriptive Process Monitoring Under
Uncertainty and Resource Constraints
Mahmoud Shoush, Marlon Dumas.
{mahmoud.shoush, marlon.dumas}@ut.ee
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2. Refresh your mind: Intervention slide
Source: shorturl.at/oPSTX

3. Motivation
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● Positive outcome:
○ Customer accepted the offer and
signed the contract.
● Negative outcome:
○ Customer declined the offer, or
launched a complaint
Triggering interventions:
• Call customer to make another
offer.

4. Problem Statement
For which cases in the process should we trigger an intervention and when in such a
way that the total gain of this intervention is maximized?
● Every intervention has a cost
and consumes resources, with
limited capacity.
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5. Existing approaches*:
● Trigger an intervention based on the probability
that a case will lead to a negative outcome.
● Fahrenkrog et al. “Fire now, fire later: alarm-based systems for prescriptive process monitoring.”
● Metzger et al . “Triggering proactive business process adaptations via online reinforcement learning.”
● Bozorgi et al. “Prescriptive process monitoring for cost-aware cycle time reduction.”
● Shoush et al. “Prescriptive process monitoring under resource constraint: A causal inference approach.”
Problem Statement
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6. Existing approaches*:
● Trigger an intervention based on the probability
that a case will lead to a negative outcome.
Existing approaches limitations:
● Quantifying the quality of the prediction scores, i.e.,
Uncertainty.
● Fahrenkrog et al. “Fire now, fire later: alarm-based systems for prescriptive process monitoring.”
● Metzger et al . “Triggering proactive business process adaptations via online reinforcement learning.”
● Bozorgi et al. “Prescriptive process monitoring for cost-aware cycle time reduction.”
● Shoush et al. “Prescriptive process monitoring under resource constraint: A causal inference approach.”
Problem Statement
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7. Existing approaches*:
● Trigger an intervention based on the probability
that a case will lead to a negative outcome.
Existing approaches limitations:
● Quantifying the quality of the prediction scores, i.e.,
Uncertainty.
● Fahrenkrog et al. “Fire now, fire later: alarm-based systems for prescriptive process monitoring.”
● Metzger et al . “Triggering proactive business process adaptations via online reinforcement learning.”
● Bozorgi et al. “Prescriptive process monitoring for cost-aware cycle time reduction.”
● Shoush et al. “Prescriptive process monitoring under resource constraint: A causal inference approach.”
Problem Statement
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● Now versus Later.

8. Existing approaches*:
● Trigger an intervention based on the probability
that a case will lead to a negative outcome.
Existing approaches limitations:
● Quantifying the quality of the prediction scores, i.e.,
Uncertainty.
● Now versus Later.
● Infinite resource capacity.
● Fahrenkrog et al. “Fire now, fire later: alarm-based systems for prescriptive process monitoring.”
● Metzger et al . “Triggering proactive business process adaptations via online reinforcement learning.”
● Bozorgi et al. “Prescriptive process monitoring for cost-aware cycle time reduction.”
● Shoush et al. “Prescriptive process monitoring under resource constraint: A causal inference approach.”
Problem Statement
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9. Approach
● Main objective is to determine
when to intervene in a given
case during its execution time
to prevent or mitigate the
effect of negative outcomes
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10. Approach: Training phase
Event log pre-processing:
● Determine intervention T that positively impacts an
outcome Y.
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11. Approach: Training phase
Ensemble predictive model:
● Probability of negative outcomes: avg_pred
● Malinin, A., Prokhorenkova, L., Ustimenko, A.: Uncertainty in gradient
boosting via ensembles. arXiv preprint arXiv:2006.10562(2020).
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12. Approach: Training phase
Ensemble predictive model:
● Probability of negative outcomes: avg_pred
● Prediction uncertainty or the total uncertainty: total_uncer*.
○ Data uncertainty: outcome overlaps.
○ Knowledge uncertainty: lack of model knowledge.
● Malinin, A., Prokhorenkova, L., Ustimenko, A.: Uncertainty in gradient
boosting via ensembles. arXiv preprint arXiv:2006.10562(2020).
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13. Approach: Training phase
Causal model:
● The effectiveness of an intervention T on an
outcome y, i.e., CATE or Uplift score.
● CATE: (Conditional average treatment effect): The
expected causal effect of the intervention:
Causal effect
(CATE)
P(-veOut | intervene=1) - P(-veOut | intervene = 0)
=
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14. Approach: Testing phase
● Filtering.
● Ranking.
● Resource allocation.
Main components:
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15. Approach: Testing phase
Filtering:
● avg_pred > 𝛕, e.g., 0.5.
● CATE > 0.
● Minimum total uncertainty.
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16. Approach: Testing phase
Ranking:
● Now versus Later:
○ c_avg_pred.
○ c_CATE.
○ c_total_uncer.
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17. Approach: Testing phase
Ranking:
● Now versus Later:
○ c_avg_pred, f_avg_pred.
○ c_CATE, f_CATE.
○ c_total_uncer, f_total_uncer.
● Gain: is the benefits we attain at one state
only, either current or future.
○ c_gain, f_gain
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● Shoush et al. “Prescriptive process monitoring under resource constraint: A causal inference approach.”
Gain* = costWithNoIntervention - costWithIntervention

18. Approach: Testing phase
Ranking:
● How to select the best case among candidates
considering current and future state for
ongoing cases ?
○ Gain: is the benefits we attain at one state
only, either current or future.
■ c_gain
■ f_gain
○ Opportunity cost: what we lose when we
intervene now versus later.
■ opp_cost = f_gain - c_gain
● Adjusted gain: is the benefits we attain,
considering current and future states.
○ adj_gain = c_gain - opp_cost
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19. Approach: Example
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20. Approach: Example
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21. Evaluation
● Data-set:
○ BPI challenge 2017.
● Predictive and causal models:
○ CatBoost.
○ Orthogonal Random Forest (ORF)
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22. Summary
● Adding temporal constraints on when
interventions can be triggered on a case.
What we did:
What is next :
● Handle multiple types of interventions.
● Experimenting with more event logs.
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