Point process models

Mrs. Young | July 17, 2025

This method also belongs to Ecology-guided Modelling.

Table of Contents

  1. Description & principle
  2. Reference articles
  3. Packages
  4. Assessment table

Description & principle

A clear, technical yet accessible explanation of the method, its core principle(s).

Major variants

optional

If the method has variants that seem important, either already widespread or promising and well documented.

Further online resources

References to useful online resources to get started, e.g. explanation blogs

Reference articles

Method

  • One or a few key academic references that introduce or formalize the method.

Research applications

With RS data in Ecology / Biodiversity

  • A

Without RS data (Ecology domain)

optional

  • B

Packages

Python

R

Code Cells

optional

Assessment table

CategoryCriteriaAssessment
OutcomeObjectiveEffect estimation, Predictive task + interpretability, Detection, Scenario projection
 EstimandCATE
 ValidityModerate confidence
Data compatibilityTypePanel data (many samples)
 Required TS length≥ 10
 Handles few samples10 to 100
 Handles huge datasets (n)Yes
 Handles missing dataPartially
 RS-data provenFew applications
AssumptionsFunctional formLinear, Quadratic, Non-linear, Additivity, Assumption-free
 No unobserved confoundersRecommended
 No interferenceRecommended
 Well-defined treatmentsRecommended, Relaxes assumption
 Common support (positivity)Recommended
 Causal Markov ConditionRequired
 FaithfulnessRequired
 IDDRelaxes assumption
 Model specific assumptionParallel trends, Stationarity, Normality of random effects, Good covariate balance, Instrument validity
Model propertiesRequires explicit processesOptional
 Exposure typeAll
 Number of variablesMultivariate
 Handles lag effectsPossible
 Propagates uncertaintyInherent capacity, Model-specific tools
 Parametric natureParametric, Semi-parametric
PackagesLanguageR, Python
 UsageUser-friendly, Technical but well documented

References