Package: causalOT 1.0.4

Introduction | Setting up the data | Estimating weights | Estimating treatment effects | Model based estimates | Linear models with lm | Barycentric projections | Note | Diagnostics | 1. Effective sample size | 2. Mean balance | 3. Distributional balance | Other methods | Further information | Options? | More complicated models via object-oriented functions

Overview | Base functions for OOP | Measure objects | OTProblem objects | A summmary of the steps to solve causal optimal transport problems | An example using federated learning for causal inference | Solving the problem directly | Data sharing restrictions exist | Only moments are available | We can pass other data that respects privacy | Option 1: pass the gradients back | Option 2: create pseudo data at each site and then pass this back to the main site | An example to generalize from one site to another | More general OT problems