synthetic-control

A Python package for causal inference using Synthetic Controls

Factor-Based Imputation for Missing Data

Causal Inference Using Quasi-Experimental Methods

A Penalized Synthetic Control Estimator for Disaggregated Data

Replication materials for the paper "Evaluating the Effect of Homicide Prevention Strategies in São Paulo, Brazil: A Synthetic Control Approach" (2016)

Replication code for "RNN-based counterfactual prediction"

Comparative Case Studies using The Synthetic Control Method

Source code to replicate the results of our article published in Revista de Historia Económica: Another case of the middle-income trap: Chile, 1900-1939

My assignments and R projects for the CS112 course. Topics covered include Predictive and Causal Inference, Genetic and P-score Matching, Synthetic Control.

This tutorial presents the analysis of the 5th chapter of my master thesis - "The Effect of Conditional Cash Transfer Policies on Crime: Evidence from a Synthetic Controls Framework" - Felipe Santos-Marquez

The ongoing pandemic of coronavirus disease 2019-2020 (COVID-19) is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). This pathogenic virus is able to spread asymptotically during its incubation stage through a vulnerable population. Given the state of healthcare, policymakers were urged to contain the spread of infection, minimize stress on the health systems and ensure public safety. Most effective tool that was at their disposal was to close non-essential business and issue a stay home order. In this paper we consider techniques to measure the effectiveness of stringency measures adopted by governments across the world. Analyzing effectiveness of control measures like lock-down allows us to understand whether the decisions made were optimal and resulted in a reduction of burden on the healthcare system. In specific we consider using a synthetic control to construct alternative scenarios and understand what would have been the effect on health if less stringent measures were adopted. We present analysis for The State of New York, United States, Italy and The Indian capital city Delhi and show how lock-down measures has helped and what the counterfactual scenarios would have been in comparison to the current state of affairs. We show that in The State of New York the number of deaths could have been 6 times higher, and in Italy, the number of deaths could have been 3 times higher by 26th of June, 2020.

Using simulated data to understand synthetic control

Code for disasters projects, regressions and synthetic controls

julia implementation of Synthetic Control estimation

Replication code for "State-Building through Public Land Disposal? An Application of Matrix Completion for Counterfactual Prediction"

Synthetic difference in differences for Python

A replication of the paper "Democratization and Economic Output in Sub-Saharan Africa".

A tool to run a pool of synthetic controls, conduct inference, and produce visualizations.