wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Finally, we estimate the weights in two applications and find that in both cases, around half of the average treatment effects receive a negativeweight .
Revised:  Finally, we estimate the weights in two applications and find that in both cases, around half of the weights attached to those regressions are negative. In one application, our new estimator is of a different sign than the first two-way fixed effects estimator we study, and significantly different from the second one .
Edit intent: meaning-changed
Original: Around 20\% of all empirical articles published by the American Economic Review between 2010 and 2012 estimate treatment effects using linear regressions with time and group fixed effects . In a model where the effect of the treatment is constant across groups and over time, such two-way fixed effects regressions identify the treatment effect of interest under the standard "common trends" assumption.
Revised: Around 20\% of all empirical articles published by the American Economic Review between 2010 and 2012 estimate treatment effects using linear regressions with time and group fixed effects are widely used to estimate the effect of a treatment on an outcome. If the effect of the treatment is constant across groups and over time, such two-way fixed effects regressions identify the treatment effect of interest under the standard "common trends" assumption.
Edit intent: clarity
Original: Linear regressions with time and group fixed effects are widely used to estimate the effect of a treatment on an outcome. If the effect of the treatment is constant across groups and over time, such regressions identify the treatment effect under the standard "common trends" assumption. But these regressions have not been analyzed yet allowing for treatment effect heterogeneity. We study two commonly used two-way fixed effectsregressions. We start by showing that without assuming constant treatment effects, those regressions identify weighted sums of the average treatment effects in each group and period, where some weights may be negative.
Revised: Linear regressions with time and group fixed effects are widely used to estimate treatment effects. We show that they identify weighted sums of the average treatment effects in each group and period, where some weights may be negative.
Edit intent: coherence
Original:  The weights can be estimated, and can help researchers assess whether their results are robust to heterogeneous treatment effects across groups and periods. When some weights are negative, their estimates may not even have the same sign as the true average treatment effect if treatment effects are heterogeneous. We then propose another estimator that does not rely on any treatment effect homogeneity assumption.
Revised:  The weights can be estimated, and can help researchers assess whether their results are robust to heterogeneous treatment effects across groups and over time. We propose another estimator that does not rely on any treatment effect homogeneity assumption.
Edit intent: clarity
Original:  We then propose another estimator that does not rely on any treatment effect homogeneity assumption. We revisit two empirical articles that have estimated two-way fixed effectsregressions. In both cases, around half of the weights attached to those regressions are negative. In one application, our new estimator is of a different sign than the first two-way fixed effects estimatorwe study, and significantly different from the second one .
Revised:  We then propose another estimator that is robust to heterogeneous treatment effects. In an application, it is of a different sign than the first two-way fixed effects estimatorwe study, and significantly different from the second one .
Edit intent: clarity
Original: Difference-in-Differences (DID) is one of the most important and popular designs for evaluating causal effects of policy changes. In its standard format, there are two time periods and two groups: in the first period no one is treated, and in the second period a "treatment group" becomes treated, whereas a "control group" remains untreated. However, many empirical applications of the DID design have more than two periods and variation in treatment timing. In this article, we consider identification and estimation of treatment effect parameters using DID with (i) multiple time periods, (ii) variation in treatment timing, and (iii) when the "parallel trends assumption" holds potentially only after conditioning on observed covariates.
Revised: In this article, we consider identification and estimation of treatment effect parameters using DID with (i) multiple time periods, (ii) variation in treatment timing, and (iii) when the "parallel trends assumption" holds potentially only after conditioning on observed covariates.
Edit intent: coherence
Original:  In this article, we consider identification and estimation of treatment effect parameters using DID with (i) multiple time periods, (ii) variation in treatment timing, and (iii) when the "parallel trends assumption" holds potentially only after conditioning on observed covariates.
Revised:  In this article, we consider identification and estimation of treatment effect parameters using Difference-in-Differences (DID) with (i) multiple time periods, (ii) variation in treatment timing, and (iii) when the "parallel trends assumption" holds potentially only after conditioning on observed covariates.
Edit intent: meaning-changed
Original:  We propose a simple two-step estimation strategy, establish the asymptotic properties of the proposed estimators, and prove the validity of a computationally convenient bootstrap procedure . Furthermore we propose a semiparametric data-driven testing procedure to assess the credibility of the DID design in our context.
Revised:  We propose a simple two-step estimation strategy, establish the asymptotic properties of the proposed estimators, and prove the validity of a computationally convenient bootstrap procedure to conduct asymptotically valid simultaneous (instead of pointwise) inference. Our proposed inference procedure naturally adjusts for autocorrelation and other forms of clustering in the data. We also propose a semiparametric data-driven testing procedure to assess the credibility of the DID design in our context.
Edit intent: meaning-changed
Original:  Finally, we analyze the effect of the minimum wage on teen employment from 2001-2007 . By using our proposed methods we confront the challenges related to variation in the timing of the state-level minimum wage policy changes .
Revised:  Finally, we illustrate the relevance of our proposed tools by analyzing the effect of the minimum wage on teen employment from 2001-2007 . By using our proposed methods we confront the challenges related to variation in the timing of the state-level minimum wage policy changes .
Edit intent: clarity
Original: In this article, we consider identification and estimationof treatment effect parameters using Difference-in-Differences (DID) with (i) multiple time periods, (ii) variation in treatment timing, and (iii) when the "parallel trends assumption" holds potentially only after conditioning on observed covariates.
Revised: In this article, we consider identification , estimation, and inference procedures for treatment effect parameters using Difference-in-Differences (DID) with (i) multiple time periods, (ii) variation in treatment timing, and (iii) when the "parallel trends assumption" holds potentially only after conditioning on observed covariates.
Edit intent: clarity
Original:  We propose a simple two-step estimation strategy, establish the asymptotic properties of the proposed estimators , and prove the validity of a computationally convenient bootstrap procedure to conduct asymptotically valid simultaneous (instead of pointwise) inference.
Revised:  We show that a family of causal effect parameters are identified in staggered DID setups, even if differences in observed characteristics create non-parallel outcome dynamics between groups. Our identification results allow one to use outcome regression, inverse probability weighting, or doubly-robust estimands. We also propose different aggregation schemes that can be used to highlight treatment effect heterogeneity across different dimensions as well as to summarize the overall effect of participating in the treatment. We establish the asymptotic properties of the proposed estimators , and prove the validity of a computationally convenient bootstrap procedure to conduct asymptotically valid simultaneous (instead of pointwise) inference.
Edit intent: meaning-changed
Original:  We propose a simple two-step estimation strategy, establish the asymptotic properties of the proposed estimators , and prove the validity of a computationally convenient bootstrap procedure to conduct asymptotically valid simultaneous (instead of pointwise) inference.
Revised:  We propose a simple two-step estimation strategy, establish the asymptotic properties of the proposed estimators and prove the validity of a computationally convenient bootstrap procedure to conduct asymptotically valid simultaneous (instead of pointwise) inference.
Edit intent: fluency
Original:  Finally, we illustrate the relevance of our proposed tools by analyzing the effect of the minimum wage on teen employment from 2001-2007 . Open-source software is available for implementing the proposed methods.
Revised:  Finally, we illustrate the relevance of our proposed tools by analyzing the effect of the minimum wage on teen employment from 2001--2007 . Open-source software is available for implementing the proposed methods.
Edit intent: fluency
Original: In this article, we consider identification, estimation, and inference procedures for treatment effect parameters using Difference-in-Differences ( DID ) with (i) multiple time periods, (ii) variation in treatment timing, and (iii) when the "parallel trends assumption" holds potentially only after conditioning on observed covariates.
Revised: In this article, we consider identification, estimation, and inference procedures for treatment effect parameters using Difference-in-Differences ( DiD ) with (i) multiple time periods, (ii) variation in treatment timing, and (iii) when the "parallel trends assumption" holds potentially only after conditioning on observed covariates.
Edit intent: fluency
Original:  In my model, the observed matching of workers to firms is the outcome of a discrete, two-sided matching process where firms with heterogeneous preference rankings over education sequentially choose workers according to an index correlated with worker preference rankings over firms- the index being a simple and flexible way to model frictions .
Revised:  The observed matching of workers to firms is the outcome of a discrete, two-sided matching process where firms with heterogeneous preference rankings over education sequentially choose workers according to an index correlated with worker preference rankings over firms- the index being a simple and flexible way to model frictions .
Edit intent: clarity
Original:  In my model, the observed matching of workers to firms is the outcome of a discrete, two-sided matching process where firms with heterogeneous preference rankings over education sequentially choose workers according to an index correlated with worker preference rankings over firms- the index being a simple and flexible way to model frictions .
Revised:  In my model, the observed matching of workers to firms is the outcome of a discrete, two-sided matching process where firms with heterogeneous preferences over education sequentially choose workers according to an index correlated with worker preference rankings over firms- the index being a simple and flexible way to model frictions .
Edit intent: fluency
Original:  In my model, the observed matching of workers to firms is the outcome of a discrete, two-sided matching process where firms with heterogeneous preference rankings over education sequentially choose workers according to an index correlated with worker preference rankings over firms- the index being a simple and flexible way to model frictions .
Revised:  In my model, the observed matching of workers to firms is the outcome of a discrete, two-sided matching process where firms with heterogeneous preference rankings over education sequentially choose workers according to an index correlated with worker preferences over firms .
Edit intent: coherence
Original:  I show how inference is possible in a procedure combining discrete choice methods with simulation-based inference. I estimate the model using data from Canada's Workplace-Employee Survey for the years 1999-2005. My counterfactual analysis of education and wages shows that changes in the matching technology can lead to economically significant equilibrium changes in both inequality and the probability of investing in higher education , and that these effects are more pronounced when worker and firm attributes are complements in the match surplus function.
Revised:  I show how inference is possible in a procedure combining discrete choice methods with simulation. Counterfactual analysis using Canadian data shows that changes in the matching technology can lead to economically significant equilibrium changes in both inequality and the probability of investing in higher education , and that these effects are more pronounced when worker and firm attributes are complements in the match surplus function.
Edit intent: clarity
Original:  My counterfactual analysis of education and wages shows that changes in the matching technology can lead to economically significant equilibrium changes in both inequality and the probability of investing in higher education , and that these effects are more pronounced when worker and firm attributes are complements in the match surplus function.
Revised:  My counterfactual analysis of education and wages shows that changes in matching frictions can lead to economically significant equilibrium changes in both inequality and the probability of investing in higher education , and that these effects are more pronounced when worker and firm attributes are complements in the match surplus function.
Edit intent: clarity
Original:  My counterfactual analysis of education and wages shows that changes in the matching technology can lead to economically significant equilibrium changes in both inequality and the probability of investing in higher education , and that these effects are more pronounced when worker and firm attributes are complements in the match surplus function.
Revised:  My counterfactual analysis of education and wages shows that changes in the matching technology can lead to economically significant equilibrium changes in both inequality and the probability of investing in higher education . These effects are more pronounced when worker and firm attributes are complements in the match surplus function.
Edit intent: coherence
Original:  I propose an inference procedure combining discrete choice methods with simulation . Counterfactual analysis using Canadian data shows that changes in matching frictions can lead to economically significant equilibrium changes in both inequality and the probability of investing in higher education.
Revised:  Although the observed matching exhibits strong cross-sectional dependence due to the matching process, we propose an asymptotically valid inference procedure that combines discrete choice methods with simulation . Counterfactual analysis using Canadian data shows that changes in matching frictions can lead to economically significant equilibrium changes in both inequality and the probability of investing in higher education.
Edit intent: meaning-changed
Original: The randomized experiment is an important tool for inferring the causal impact of an intervention. The recent literature on statistical learning methods for heterogeneous treatment effects demonstrates the utility of estimating the marginal conditional average treatment effect (MCATE), i.e., the average treatment effect for a subpopulation of respondents who share a particular subset of covariates.
Revised: In the recent literature on statistical learning methods for heterogeneous treatment effects demonstrates the utility of estimating the marginal conditional average treatment effect (MCATE), i.e., the average treatment effect for a subpopulation of respondents who share a particular subset of covariates.
Edit intent: coherence
Original:  The recent literature on statistical learning methods for heterogeneous treatment effects demonstrates the utility of estimating the marginal conditional average treatment effect (MCATE), i.e., the average treatment effect for a subpopulation of respondents who share a particular subset of covariates.
Revised:  The recent literature on estimating heterogeneous treatment effects demonstrates the utility of estimating the marginal conditional average treatment effect (MCATE), i.e., the average treatment effect for a subpopulation of respondents who share a particular subset of covariates.
Edit intent: clarity
Original:  The recent literature on statistical learning methods for heterogeneous treatment effects demonstrates the utility of estimating the marginal conditional average treatment effect (MCATE), i.e., the average treatment effect for a subpopulation of respondents who share a particular subset of covariates. However, each proposed method makes its own set of restrictive assumptions about the intervention's effects , the underlying data generating processes, and which subpopulations (MCATEs) to explicitly estimate.
Revised:  The recent literature on statistical learning methods for heterogeneous treatment effects , each proposed method makes its own set of restrictive assumptions about the intervention's effects , the underlying data generating processes, and which subpopulations (MCATEs) to explicitly estimate.
Edit intent: clarity
Original:  We frame this challenge as a pattern detection problem where we  maximize a nonparametric scan statistic (measurement of distributional divergence) over subpopulations, while being parsimonious in which specific subpopulations to evaluate .
Revised:  We frame this challenge as a pattern detection problem where we efficiently maximize a nonparametric scan statistic (measurement of distributional divergence) over subpopulations, while being parsimonious in which specific subpopulations to evaluate .
Edit intent: clarity
Original:  In addition to the algorithm, we demonstrate that the asymptotic Type I and II error can be controlled, and provide sufficient conditions for detection consistency---i .e., exact identification of the affected subpopulation.
Revised:  In addition to the algorithm, we demonstrate that the asymptotic Type I and II error can be controlled, and provide sufficient conditions for detection consistency--i .e., exact identification of the affected subpopulation.
Edit intent: fluency
Original:  symmetry breaking from the stationary centrosymmetric to the well-known steadily propagating crescent shape, (ii)  response to mechanical stress externally applied to the substrate (tensotaxis), ( iii ) changing direction of motion towards an interface with a rigid substrate (durotaxis) and ( iv ) the configuration of substrate wrinkles induced by contractile forces applied by the keratocyte.
Revised:  symmetry breaking from the stationary centrosymmetric to the well-known steadily propagating crescent shape, (ii) asymmetric bipedal oscillations and traveling waves in the lamellipodium leading edge (iii) response to mechanical stress externally applied to the substrate (tensotaxis), ( iii ) changing direction of motion towards an interface with a rigid substrate (durotaxis) and ( iv ) the configuration of substrate wrinkles induced by contractile forces applied by the keratocyte.
Edit intent: meaning-changed
Original:  symmetry breaking from the stationary centrosymmetric to the well-known steadily propagating crescent shape, (ii)  response to mechanical stress externally applied to the substrate (tensotaxis), ( iii ) changing direction of motion towards an interface with a rigid substrate (durotaxis) and ( iv ) the configuration of substrate wrinkles induced by contractile forces applied by the keratocyte.
Revised:  symmetry breaking from the stationary centrosymmetric to the well-known steadily propagating crescent shape, (ii) response to mechanical stress externally applied to the substrate (tensotaxis), ( iv ) changing direction of motion towards an interface with a rigid substrate (durotaxis) and ( iv ) the configuration of substrate wrinkles induced by contractile forces applied by the keratocyte.
Edit intent: fluency
Original:  symmetry breaking from the stationary centrosymmetric to the well-known steadily propagating crescent shape, (ii)  response to mechanical stress externally applied to the substrate (tensotaxis), ( iii ) changing direction of motion towards an interface with a rigid substrate (durotaxis) and ( iv ) the configuration of substrate wrinkles induced by contractile forces applied by the keratocyte.
Revised:  symmetry breaking from the stationary centrosymmetric to the well-known steadily propagating crescent shape, (ii) response to mechanical stress externally applied to the substrate (tensotaxis), ( iii ) changing direction of motion towards an interface with a rigid substrate (durotaxis) and ( v ) the configuration of substrate wrinkles induced by contractile forces applied by the keratocyte.
Edit intent: clarity
Original:  First, KiNetX  identifies and eliminates all kinetically irrelevant species and elementary reactions to guide and, thereby, accelerate the exploration of chemical reaction space.
Revised:  First, KiNetX evaluates the kinetic relevance of every species in a (yet incomplete) reaction network to confine the search for new elementary reaction steps only to those species that are considered possibly relevant. Second, KiNetX identifies and eliminates all kinetically irrelevant species and elementary reactions to guide and, thereby, accelerate the exploration of chemical reaction space.
Edit intent: meaning-changed
Original:  First, KiNetX  identifies and eliminates all kinetically irrelevant species and elementary reactions to guide and, thereby, accelerate the exploration of chemical reaction space. Second, KiNetX propagates the correlated uncertainty in the network parameters (activation free energies) obtained from ensembles of quantum chemical models to infer the uncertainty of product distributions and entire reaction mechanisms .
Revised:  First, KiNetX identifies and eliminates all kinetically irrelevant species and elementary reactions to reduce a complex network graph to a comprehensible mechanism .
Edit intent: clarity
Original:  Third, KiNetX estimates the sensitivity of species concentrations toward changes in individual activation free energies , which allows us to systematically select the most efficient quantum chemical model for each elementary reaction given a predefined accuracy.
Revised:  Third, KiNetX estimates the sensitivity of species concentrations toward changes in individual rate constants (derived from relative free energies) , which allows us to systematically select the most efficient quantum chemical model for each elementary reaction given a predefined accuracy.
Edit intent: meaning-changed
Original:  Third, KiNetX estimates the sensitivity of species concentrations toward changes in individual activation free energies , which allows us to systematically select the most efficient quantum chemical model for each elementary reaction given a predefined accuracy.
Revised:  Third, KiNetX estimates the sensitivity of species concentrations toward changes in individual activation free energies , which allows us to systematically select the most efficient electronic structure model for each elementary reaction given a predefined accuracy.
Edit intent: meaning-changed