Overview

CausalELM enables Estimation of causal quantities of interest in research designs where a counterfactual must be predicted and compared to the observed outcomes. More specifically, CausalELM provides structs and methods to execute event study designs (interupted time series analysis), G-Computation, and doubly robust estimation as well as estimation of the CATE via S-Learning, T-Learning, and X-Learning. In all of these implementations, CausalELM predicts the counterfactuals using an Extreme Learning Machine. In this context, ELMs strike a good balance between prediction accuracy, generalization, ease of implementation, speed, and interpretability. In addition, CausalELM provides the ability to incorporate an L2 penalty.

Installation

using Pkg
Pkg.add("CausalELM")

Estimating Causal Effects

using CausalELM

# 1000 data points with 5 features in pre-event period
x0 = rand(1000, 5)

# Pre-event outcome
y0 = rand(1000)

# 200 data points in the post-event period
x1 = rand(200, 5)

# Pose-event outcome
y1 = rand(200)

# Instantiate an EventStudy struct
event_study = EventStudy(x0, y0, x1, y1)

estimatecausaleffect!(event_study)

summarize(event_study)