Introduction
Jared Amani Greathouse
- PhD Candidate, Public Policy, Georgia State University
- Advisor: Jason Coupet
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Policy Challenges Motivation
- Public policy increasingly relies on causal inference to evaluate complex interventions
- Many high-stakes policies create data environments where:
- Donor pools/sets of untreated units are high dimensional
- Spillovers are present, or
- Valid geographic controls do not exist
- These settings strain or invalidate traditional empirical tools, which relax one or many of these constraints.
Thesis:
Machine learning augmentations of traditional causal methods (e.g., ITS, SCM) can overcome these challenges, providing more credible inference in high-stakes policy settings
Technical Challenges/Motivation
Many cutting-edge causal inference methods (e.g., SHC, RESCM, augmented SCM) do not have ready-to-use software
Existing tools (SCM packages in R/Stata/Python) are limited in scope and cannot implement novel designs.
To fill this gap, I developed mlsynth, an open-source Python package
mlsynth enables:
Synthetic control methods and panel data approaches in a simple and flexbile manner
Implements machine learning methods to address spillovers/SUTVA violations, high-dimensional donor pools, etc.
Paper 1: Hawaii’s COVID-19 Quarantine
Question:
What were the short-run economic costs of Hawaii’s March 2020 mandatory quarantine?
Method:
Synthetic Historical Control (SHC)
Outcomes:
- Tourism demand
- Labor market conditions
- Statewide economic activity
Why Hawaii?
- Most extreme U.S. COVID containment policy
- Introduced a mandatory 14-day quarantine on all arrivals (March 26, 2020)
- Effectively sealed borders and shut down tourism
- No comparable U.S. state intervention
Core Policy Question
What are the economic costs of decisive early action under uncertainty?
- Early containment may prevent long-run damage
- But imposes large short-run economic losses
- Critical for future crises:
- Pandemics
- Climate shocks
- Geopolitical disruptions
- A key question is the degree to which the intervention impacted the economy, in contrast to places like Florida who did not such restrictions.
Difficulty in studying this case
The Identification Problem
- COVID-19 is a global shock
- Nearly all regions experienced treatment
- No clean donor pool exists
- Cross-sectional comparisons break down
Methodological Solution
Synthetic Historical Control (SHC)
- Uses the treated unit’s own past
- Constructs counterfactuals from historical segments
- Requires no geographic control units
- Designed for:
- One-off shocks
- System-wide interventions
SHC Intuition
- Match the last pre-treatment window
- Using weighted combinations of:
- Earlier historical segments where intervention was not active.
- Learn how Hawaii typically evolves, absent either pandemic or lockdown
- Extrapolate forward absent treatment
Outcomes
Tourism Demand - Visitor Days - Hotel Occupancy
Economic Conditions - Accommodation Employment
All measured as: - Annualized year-over-year growth rates
Data
- Monthly time series
- ~30 years of pre-treatment data (1990 to 2020).
- Sources:
- Treatment begins: March 2020
Inference
- Placebo tests in time
- Conformal prediction intervals
- Distribution-free
- 90% out-of-sample coverage
Contribution
- First causal estimate of Hawaii’s quarantine effects
- Demonstrates SHC in an extreme policy setting
- Provides evidence on economic costs of near-zero-COVID policies
- Broad relevance for crisis policymaking
Paper 2: Impact of National Lockdown on Air Quality in India
Motivation
- Air pollution is one of India’s most severe public health challenges
- Fine particulate matter (PM2.5) is the primary pollutant of concern
- Chronic exposure affects over 1.4 billion people
- Persistent exceedance of WHO and national standards
Sources of PM2.5 in India
- Vehicular emissions
- Industrial production
- Construction activity
- Biomass burning (household and agricultural)
- Seasonal and meteorological factors
Severity and Spatial Variation
- National average PM2.5 far exceeds WHO guidelines
- Large regional heterogeneity:
- Indo-Gangetic Plain: extreme winter pollution
- Major metros consistently among the world’s most polluted
- Pollution is persistent, not episodic
Health Impacts
- Long-term exposure increases:
- Cardiovascular disease
- Respiratory illness
- Stroke and lung cancer
- Air pollution causes ~1.5 million premature deaths annually
- Substantial reductions in life expectancy, especially in Delhi
Economic Impacts
- Significant healthcare expenditures
- Reduced labor productivity
- Losses in tourism and agriculture
- Estimated costs ≈ 1–1.5% of GDP annually
- Policy response: National Clean Air Programme (NCAP)
The COVID-19 Lockdown
- Nationwide lockdown began March 25, 2020
- Among the strictest globally
- Sudden halt to:
- Transportation
- Industrial activity
- Construction
- A large, abrupt emissions shock
Lockdown as a Natural Experiment
- Policy applied uniformly across the country
- Sharp timing and clear intervention onset
- A rare opportunity to study large-scale emissions reductions
- Particularly relevant for national-level air quality
Observed Changes During Lockdown
- PM2.5 declined sharply across monitored locations
- Reductions often in the range of 30–50%
- Major metros experienced large AQI improvements
- Effects strongest in early lockdown phases
What Existing Studies Show
- Consistent evidence of pollution declines during lockdowns
- Findings documented across:
- Effects observed for PM2.5, NO₂, and PM10
Dominant Empirical Approaches
- Pre–post comparisons
- Year-over-year comparisons
- Graphical trend analysis
- Satellite imagery
- Simple regression models
Limits of Existing Evidence
- Largely descriptive
- No explicit counterfactual construction
- Confounding from seasonality and weather
- Limited policy relevance
Causal Approaches in the Literature
- Interrupted Time Series (ITS)
- Synthetic Control and augmented SCM
- Causal machine learning methods
- Mixed findings across contexts
Why National Causal Inference Is Hard
- Lockdowns affect all units simultaneously
- No viable untreated donor pool
- Parallel trends assumptions are implausible
- Covariates cannot fully capture meteorology
- Trditionally, practitioners would do t-tests/simpler methods.
Methodological Gap
- Few credible causal designs for national-scale interventions
- Existing tools struggle when:
- Treatment is universal
- Meaningful geographic controls do not exist
- Limits inference for major policy shocks
Data
- Source: SHRUG – Socioeconomic High-resolution Rural-Urban Geographic Data Platform for India
- Spatial coverage: 635 districts across India
- Temporal coverage: Jan 1999 – Dec 2020 (monthly observations)
- Outcome: PM2.5 concentrations, population-weighted at district level
National-level analysis: - Population-weighted average across all districts → single monthly time series
Treated districts (city-level analysis): - Delhi, Mumbai, Bangalore, Kolkata (major tech hubs/metropolitan areas). - Aggregated subdistricts to district-level averages
Contribution of This Chapter
- First causal evaluation of India’s nationwide lockdown on PM2.5
- Uses SHC:
- No contemporaneous control units
- Counterfactual built from historical patterns
- Extends SHC to an augmented framework
What This Enables
- National-level causal estimates
- Policy-relevant counterfactual inference
Paper 3: Locking Away Prosperity? Evaluating the Labor Impacts of Vaccine Mandates
Motivation
- COVID-19 NPIs initially focused on lockdowns and masks
- Early research emphasized timing and public health benefits
- Subsequent work documented economic and labor market costs
- Less is known about later-stage pandemic policies
From Lockdowns to Mandates
- Vaccines became widely available by early 2021
- Policymakers sought alternatives to broad lockdowns
- Vaccine mandates emerged as a targeted intervention
- Goal: protect public health while sustaining economic activity
Why Focus on Restaurants?
- High-contact, indoor settings
- Central to urban leisure economies
- Sensitive to both consumer confidence and labor supply
- A leading concern during mandate debates
Policy Question
How did city-level vaccine mandates affect restaurant employment?
- Did mandates slow employment recovery?
- Or did they support demand by improving perceived safety?
- Are effects consistent across cities?
Treated Cities
- New York City
- San Francisco
- New Orleans
- Los Angeles
Each implemented vaccine mandates for indoor dining in 2021. Other cities (i.e., Chicago) did too, but they did so when a new COVID variant (Omicron) became viral, a critical confounder for clean identification.
Why These Cities Matter
- Large, economically important metros
- Early adopters of vaccine mandates
- Clear policy onsets and enforcement timelines
- Urban outliers in the national policy landscape
Expected Employment Channels
- Supply side:
- Worker compliance or exit
- Staffing frictions
- Demand side:
- Consumer confidence
- Willingness to dine indoors
Net effects are theoretically ambiguous.
Contribution of This Chapter
- First causal analysis of vaccine mandates on restaurant employment
- Focuses on labor market outcomes, not health metrics
- Evaluates mandates as an economic policy tool
Empirical Strategy
- Compare treated cities to synthetic counterfactuals
- Use MSAs without similar mandates as donors
- Focus on year-over-year employment growth
- Separate analysis by city
Data
- Monthly MSA-level employment data
- Source: BLS / FRED
- Time span: 1991–2022
- Outcome:
- Full-service restaurant employment
- Leisure and hospitality employment
Why This Design Works
- Sharp policy timing
- No national mandate during study period
- Rich pre-treatment histories
- Comparable untreated metropolitan areas