Data for: Estimating causal effects with machine learning: A guide for ecologists

This repository contains the R code and data (simulated and empirical)
used for the manuscript “Estimating Causal Effects with Machine Learning:
A Guide for Ecologists.”  It provides reproducible examples
demonstrating the application of four causal machine learning
methods.  The dataset includes: Simulated data generated in R to
estimate the causal effect of honeybee abundance on wild bee populations.
Variables include environmental covariates (e.g., soil, climate,
topography), confounders (e.g., pollinated agriculture), an instrumental
variable (beekeeping policy), and outcome measures (wild bee abundance),
and include a mixture of linear, nonlinear, and interactions. 
Empirical data and example scripts illustrating the use of Causal Forests
to assess heterogeneous effects of depth on Laminaria digitata abundance
across Atlantic Canada, incorporating geographic (latitude, longitude) and
biotic (invasive bryozoan) covariates. Annotated R scripts implementing
DML, TMLE, nonlinear IV (Deep IV–inspired), and Causal Forest
workflows.  The dataset is designed for reuse by researchers
interested in learning or applying causal machine learning in ecology or
related disciplines. All data are either simulated or derived from
publicly available sources and contain no sensitive, confidential, or
personally identifiable information. The materials are released for open
reuse and adaptation, facilitating transparent and replicable applications
of causal inference in ecology.