Trajectory Balancing: A General Reweighting Approach to Causal Inference With Time-Series Cross-Sectional Data

Hazlett, Chad; Xu, Yiqing

doi:10.2139/ssrn.3214231

Cited by 42 publications

(74 citation statements)

References 18 publications

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“…Once a unit adopts the treatment, it remains exposed to the treatment for all periods afterwards. This set up is like that in Abraham and Sun [2018], Hazlett and Xu [2018], and in contrast to most of the DID literature where the binary indicator whether a unit is exposed to the treatment in the current period indexes the potential outcomes. We observe for each unit in the population the adoption date A i ∈ A and the sequence of T realized outcomes,…”

Section: Set Upmentioning

confidence: 99%

“…Collectively these two assumptions imply that we can think of the treatment as a binary one, the only relevant component of the adoption date being whether a unit is exposed to the treatment at the time we measure the outcome. Versions of such assumptions are also considered in Borusyak and Jaravel [2016], de Chaisemartin and D'Haultfoeuille [2018], Abraham and Sun [2018], Hazlett and Xu [2018] and Imai and Kim [2016], where in the latter a graphical approach is taken in the spirit of the work by Pearl [2000].…”

Section: Exclusion Restrictionsmentioning

confidence: 99%

“…Our paper is most closely relateds to a very interesting set of recent papers on DID methods that explicitly focus on issues with heterogenous treatment effects (Abraham and Sun [2018], de Chaisemartin and D'Haultfoeuille [2018], Han [2018], Goodman-Bacon [2017], Callaway and Sant'Anna [2018], Hull [2018], Strezhnev [2018], Imai et al [2018], Hazlett and Xu [2018], and Borusyak and Jaravel [2016]). Among other things these papers derive interpretations of the DID estimator as weighted averages of causal effects and bias terms under various assumptions.…”

mentioning

confidence: 91%

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Design-based Analysis in Difference-In-Differences Settings with Staggered Adoption

2018

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In this paper we study estimation of and inference for average treatment effects in a setting with panel data. We focus on the setting where units, e.g., individuals, firms, or states, adopt the policy or treatment of interest at a particular point in time, and then remain exposed to this treatment at all times afterwards. We take a design perspective where we investigate the properties of estimators and procedures given assumptions on the assignment process. We show that under random assignment of the adoption date the standard Difference-In-Differences estimator is is an unbiased estimator of a particular weighted average causal effect. We characterize the properties of this estimand, and show that the standard variance estimator is conservative.

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Section: Set Upmentioning

confidence: 99%

Section: Exclusion Restrictionsmentioning

confidence: 99%

mentioning

confidence: 91%

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Design-based Analysis in Difference-In-Differences Settings with Staggered Adoption

2018

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“…They argue that this method is particularly useful when the SCM method provides a poor fit in the pre-intervention period. Hazlett and Xu (2018) estimate the weights using a kernel transformation of the pre-intervention outcomes. This is done to ensure that higher-order features of the outcomes (authors mention, e.g., volatility and variance) are taken into account when estimating the weights.…”

Section: Synthetic Control-type Approachesmentioning

confidence: 99%

Assessing the Causal Effect of Binary Interventions from Observational Panel Data with Few Treated Units

Samartsidis¹,

Seaman²,

Presanis³

et al. 2019

Statist. Sci.

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Researchers are often challenged with assessing the impact of an intervention on an outcome of interest in situations where the intervention is non-randomised, the intervention is only applied to one or few units, the intervention is binary, and outcome measurements are available at multiple time points. In this paper, we review existing methods for causal inference in these situations. We detail the assumptions underlying each method, emphasize connections between the different approaches and provide guidelines regarding their practical implementation. Several open problems are identified thus highlighting the need for future research.

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“…Con el fin de evaluar si las diferencias observadas en el Cuadro 2 se explican por la Ley Páez, se estiman modelos de balanceo de trayectorias basados en Hazlett y Xu (2018). Esta metodología crea un grupo sintético de municipios de control que tienen un comportamiento de la variable dependiente similar al del grupo de tratados, en el periodo que precede a la ley.…”

Section: Metodologíaunclassified

Exenciones tributarias y desarrollo regional: evidencia de Colombia

Galvis–Aponte¹,

Bonilla‐Mejía²,

Gómez-Mesa³

et al. 2019

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se agradece la colaboración de Fredy Navarrete y William Galvis, así como la invaluable asistencia de Ana Pirela Ríos. Los comentarios o sugerencias a este documento pueden ser enviados a lgalviap@banrep.gov.co. AbstractThis document aims to evaluate the impact of tax incentives granted to companies on regional development by studying the Páez Law in Colombia. This law was promulgated to attract firms to the region affected by the earthquake and the avalanche of the Páez River in 1994. This is a particularly interesting natural experiment since the law arose from an unexpected shock and numerous municipalities that were not directly affected by the disaster were covered by its benefits. This allows us to differentiate the effect of tax exemptions from other factors related to the tragedy, such as property damage and aid to the victims. The causal effects are identified by comparing the municipalities that are beneficiaries of the law, affected and not affected, with a group of synthetic controls built on the trajectory balancing methodology. The results indicate that exemption policies had no major effects, either on local public finances or on the quality of life. In a complementary exercise, the effect of the Quimbaya Act of 1999 is evaluated, finding similar results. This Law benefited the municipalities affected by the Coffee Belt earthquake.

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Trajectory Balancing: A General Reweighting Approach to Causal Inference With Time-Series Cross-Sectional Data

Cited by 42 publications

References 18 publications

Design-based Analysis in Difference-In-Differences Settings with Staggered Adoption

Design-based Analysis in Difference-In-Differences Settings with Staggered Adoption

Assessing the Causal Effect of Binary Interventions from Observational Panel Data with Few Treated Units

Exenciones tributarias y desarrollo regional: evidencia de Colombia

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