Monte Carlo

Mrs. Young | July 17, 2025

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
OutcomeObjectiveSignificance and robustness tests
 EstimandATT, ATE, LATE, CATE, Mediation effects, Oriented link, Risk ratios, Maps & generalisations, Others
 ValidityExternal, Moderate confidence, Internal, Varying
Data compatibilityTypeInapplicable
 Required TS lengthInapplicable
 Handles few samplesInapplicable
 Handles huge datasets (n)Inapplicable
 Handles missing dataInapplicable
 RS-data provenFew applications
AssumptionsFunctional formInapplicable
 No unobserved confoundersInapplicable
 No interferenceInapplicable
 Well-defined treatmentsInapplicable
 Common support (positivity)Inapplicable
 Causal Markov ConditionRecommended
 FaithfulnessRecommended
 IDDInapplicable
 Model specific assumptionStationarity, Instrument validity, Normality of random effects
Model propertiesRequires explicit processesOptional
 Exposure typeAll
 Number of variablesHigh-dimensional (p≫n), Multivariate, Bivariate, Univariate
 Handles lag effectsNo, Possible, Yes
 Propagates uncertaintyInherent capacity
 Parametric natureRule-based, Non-parametric, Semi-parametric, Parametric
PackagesLanguageR, Python
 UsageTechnical but well documented

References