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NaviDAM
Home
About
Criteria
Outcome
Objective
Estimand
Validity
Data compatibility
Data type
Required TS length
Handles few samples
Handles huge datasets
Handles missing data
RS-data proven
Assumptions
Functional form
No unobserved confounders
No interference
Well-defined treatment
Common support
Causal Markov condition
Faithfulness
IDD
Model-specific
Model properties
Explicit process required
Exposure type
Number of variables
Handles lag effects
Propagates uncertainty
Parametric nature
Packages
Language
Usage
Methods
Counterfactual & future simulations
Causal generative modeling
Climate change attribution methods
Fingerprint models
Non-fingerprint models
Dynamic vegetation models
FATE-HD
LPJ-GUESS
ORCHIDEE
Ecosystem Process Models
Biogeochemistry models
Ecopath (EwE)
PREBAS
Individual-based models
RangeShifter
Land use change models
CLUE models
SLEUTH model
Process-based methods
Phenology models
Population models
RIVPACS
Experiments
Gradient / Stratified analyses
RCTs
Randomised saturation designs
Randomized encouragement designs
Ecology-guided Modelling
Linear regressions & extensions
GAMs
GLMMs, GAMMs
GLMs, GEEs
Quantile regression
Predictive models + interpretability metrics
DL + SHAP
RF + variance partition
SEMs
Variance partition
Adjusted methods (Backdoor C.)
Alternative effect identification methods
Do-calculus rules
Frontdoor criterion
Doubly robust estimation
Linear path method
Mediation & moderator analysis
Quasi-experiments
Synthetic controls
DiD & BACI
First-difference estimator
Instrumental variables
Interrupted time series
Long difference estimator
Matching methods
Matrix completion
Panel designs, TWFE
RDD (LATE)
Weighting & Propensity scores
Causal ML
BART
Causal GNNs
Causal forests
Double Machine learning
Meta-learners
R-learner, TMLE, MTP
S,T, X learners
Target transformation (F-learner)
NNs
Physics/ecology informed neural networks (PINNs)
Reinforcement learning
Causal discovery
Asymmetry-based
Additive noise models
Kernel methods
LiNGAM
Bayesian network learning
Constraint-based methods
FCI, TsFCI
PC, PCMCI
Continuous optimization
DYNOTEARS
Prediction-based approaches
CCM
Granger causality
Invariant causal prediction
S-Map
Score-based methods
GES, TsGFCI
Alternative paradigms
Bayesian Structured TS
Causal diffusion models
Generalized Dissimilarity Models (GDMs)
Info-geom. causal inference
Intermediate confounding
G methods
Marginal structural models
Prediction-based approaches
CCM
Granger causality
Invariant causal prediction
S-Map
SES & Network analysis (~adjusted method)
Independent detection
Autoregressive state-space models
DL change detection
Frequently monitored indices
Diversity metrics, turnover, variance, phenology, sentinels
Markov switching autoregression models
Outlier detection
Preliminary data correction
Frescalo
Lowess Regression
Occupancy models
Point process models
RS breakpoint detection
BFast
LandTrendr
Vegetation Change Tracker (VCT)
Trend detection
Linear, quadratic
TRIM
Versatile tools
Causal interpretability
Counterfactual explanations
Robustness & significance tests
Placebo tests
Power analyses
Sensitivity analyses
Uncertainty tools
Bootstrapping
Conformal inference
Monte Carlo
Partial identification
Visualisation helpers
Best practice boxes
Covariate timeline
Reporting measures
Treatment assignment graph
Good practices
A primer on causal graphs
Causal paradigms
Page example
Gallery
Gallery example
Contributing
NaviDAM on GitHub
Methods
Causal ML
Meta-learners
Meta-learners
Table of contents
R-learner, TMLE, MTP
S,T, X learners
Target transformation (F-learner)