synthetic-control
There are 1 repository under synthetic-control topic.
OscarEngelbrektson / SyntheticControlMethods
A Python package for causal inference using Synthetic Controls
Language:Python 161station-10 / awesome-marketing-machine-learning
A curated list of awesome machine learning libraries for marketing, including media mix models, multi touch attribution, causal inference and more
MasaAsami / pysynthdid
Synthetic difference in differences for Python
Language:Jupyter Notebook 58- Language:R 51
jeremylhour / pensynth
A Penalized Synthetic Control Estimator for Disaggregated Data (JASA, 2021)
Language:MATLAB 32nppackages / scpi
Prediction and inference procedures for synthetic control methods with multiple treated units and staggered adoption.
Language:R 28- Causal-Inference-Using-Quasi-Experimental-Methods
LeihuaYe / Causal-Inference-Using-Quasi-Experimental-Methods
Causal Inference Using Quasi-Experimental Methods
danilofreire / homicides-sp-synth
Replication materials for the paper "Evaluating the Effect of Homicide Prevention Strategies in São Paulo, Brazil: A Synthetic Control Approach" (2016)
Language:TeX 5jvpoulos / rnns-causal
Replication code for "RNN-based counterfactual prediction, with an application to homestead policy and public schooling"
Language:R 5wxwx1993 / wildfire_mitigation
data repository for publicly available code and data of "Low-intensity fires mitigate the risk of catastrophic wildfires in California's forests"
Language:R 5JBris / synthetic-control-pymc
Tutorials for the synthetic control method for causal inference using PyMC
Language:Jupyter Notebook 3- Synthetic_Control_Method
Tahahaha7 / Synthetic_Control_Method
Comparative Case Studies using The Synthetic Control Method
Language:Jupyter Notebook 3 cvas91 / Cannabis_Legalization
Prevalence of Cannabis Use Disorder Post Legalization – Uruguay Case Study (Synthetic Control Method)
Language:Stata 2justinjjlee / julia-SyntheticControl.jl
julia implementation of Synthetic Control estimation
Language:Julia 2agoryuno / robust_control
A PyTorch implementation of the "robust" synthetic control model
Language:Python 1alexanderthclark / Synthetic-Control-Simulation
Using simulated data to understand synthetic control
Language:Jupyter Notebook 1almagashi / Replication-and-Extension
A replication of the paper "Democratization and Economic Output in Sub-Saharan Africa".
Language:HTML 1diodz / chile-middle-income-trap
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
Language:Python 1jguerrae / Escape-Clauses_SynthControl
This repository implements synthetic control methodology to estime the effect of active escape clauses on debt.
Language:Jupyter Notebook 1Replication code for "State-Building through Public Land Disposal? An Application of Matrix Completion for Counterfactual Prediction"
Language:R 1kenki931128 / synthx
SynthX: A Python Library for Advanced Synthetic Control Analysis
Language:Python 1- Language:Ruby 1
15EkbergC / Synthetic-Control-for-Elon-Musk-Buying-Twitter
Using the Synthetic Control method to effectively demonstrate the impact of Elon Musk purchasing Twitter on overall sentiment towards the company and on Twitter's stock price.
Language:Jupyter Notebook 0diloloan72 / cs112
My assignments and R projects for the CS112 course. Topics covered include Predictive and Causal Inference, Genetic and P-score Matching, Synthetic Control.
Language:R 0Code for disasters projects, regressions and synthetic controls
Language:Python 0subhaskghosh / lockdown-paper
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.
Language:Python 0murattasdemir / synth_runner
A tool to run a pool of synthetic controls, conduct inference, and produce visualizations.
Language:Stataquarcs-lab / Tutorial-synthetic-control-methods-Felipe-thesis-chapter5
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
Language:Rsodascience / schools_synth_test
Test code for disaggregated synthetic control with simulated data
Language:R
