Optimal Transport Weights for Causal Inference
Barycentric Projection outcome estimation
Estimate causal weights
causalEffect class
An R package to perform causal inference using optimal transport dista...
causalWeights class
Extract treatment effect estimate
cot_solve method for ateClass objects
cot_solve for gridSearch
cot_solve method for likelihoodMethods
Options available for the COT method
Title
dataHolder-class
dataHolder-methods
dataHolder
CRASH3 data example
Hainmueller data example
R6 Data Generating Parent Class
df2dataHolder-methods
df2dataHolder
Options for the Entropy Balancing Weights
Effective Sample Size
Estimate treatment effects
Function to estimate outcome models
gridSearch S4 class
LaLonde data example
Standardized absolute mean difference calculations
An R6 Class for setting up measures
An R6 object for measures
Internal function to select appropriate loss function
Optimal Transport Distance
Object Oriented OT Problem
An R6 class to construct OTProblems
plot.causalWeights
Predict method for barycentric projection models
print.dataHolder
PSIS casualWeights class
Pareto-Smoothed Importance Sampling
Options for the SBW method
Options for the SCM Method
Summary diagnostics for causalWeights
Supported Methods
Get the variance of a causalEffect
Uses optimal transport distances to find probabilistic matching estimators for causal inference. These methods are described in Dunipace, Eric (2021) <arXiv:2109.01991>. The package will build the weights, estimate treatment effects, and calculate confidence intervals via the methods described in the paper. The package also supports several other methods as described in the help files.