wanyu/IteraTeR_full_sent huggingface dataset:
Original:  To address this issue, we leverage insights from the literature on ambiguity and model uncertainty to propose a tractable econometric framework for characterizing the sensitivity of counterfactuals with respect to a researcher's assumptions about the distribution of unobservables in a class of structural models.
Revised:  To address this issue, we leverage insights from the literature on ambiguity and model uncertainty to propose a tractable econometric framework for characterizing the sensitivity of counterfactuals with respect to parametric assumptions about the distribution of unobservables in a class of structural models.
Edit intent: clarity
Original:  To address this issue, we leverage insights from the literature on ambiguity and model uncertainty to propose a tractable econometric framework for characterizing the sensitivity of counterfactuals with respect to a researcher's assumptions about the distribution of unobservables in a class of structural models.
Revised:  To address this issue, we leverage insights from the literature on ambiguity and model uncertainty to propose a tractable econometric framework for characterizing the sensitivity of counterfactuals with respect to a researcher's assumptions about the distribution of latent variables in a class of structural models.
Edit intent: clarity
Original:  In particular, we show how to construct the smallest and largest values of the counterfactual as the distribution of unobservables spans nonparametric neighborhoods of the researcher's assumed specification while other `structural' features of the model , e.g. equilibrium conditions, are maintained.
Revised:  In particular, we show how to characterize the smallest and largest values of the counterfactual as the distribution of unobservables spans nonparametric neighborhoods of the researcher's assumed specification while other `structural' features of the model , e.g. equilibrium conditions, are maintained.
Edit intent: clarity
Original:  In particular, we show how to construct the smallest and largest values of the counterfactual as the distribution of unobservables spans nonparametric neighborhoods of the researcher's assumed specification while other `structural' features of the model , e.g. equilibrium conditions, are maintained.
Revised:  In particular, we show how to construct the smallest and largest values of the counterfactual as the distribution of latent variables spans nonparametric neighborhoods of the researcher's assumed specification while other `structural' features of the model , e.g. equilibrium conditions, are maintained.
Edit intent: clarity
Original:  In particular, we show how to construct the smallest and largest values of the counterfactual as the distribution of unobservables spans nonparametric neighborhoods of the researcher's assumed specification while other `structural' features of the model , e.g. equilibrium conditions, are maintained.
Revised:  In particular, we show how to construct the smallest and largest values of the counterfactual as the distribution of unobservables spans nonparametric neighborhoods of a researcher's assumed specification while other `structural' features of the model , e.g. equilibrium conditions, are maintained.
Edit intent: fluency
Original:  In particular, we show how to construct the smallest and largest values of the counterfactual as the distribution of unobservables spans nonparametric neighborhoods of the researcher's assumed specification while other `structural' features of the model , e.g. equilibrium conditions, are maintained.
Revised:  In particular, we show how to construct the smallest and largest values of the counterfactual as the distribution of unobservables spans nonparametric neighborhoods of the researcher's parametric specification while other `structural' features of the model , e.g. equilibrium conditions, are maintained.
Edit intent: clarity
Original:  In particular, we show how to construct the smallest and largest values of the counterfactual as the distribution of unobservables spans nonparametric neighborhoods of the researcher's assumed specification while other `structural' features of the model , e.g. equilibrium conditions, are maintained.
Revised:  In particular, we show how to construct the smallest and largest values of the counterfactual as the distribution of unobservables spans nonparametric neighborhoods of the researcher's assumed specification while other "structural" features of the model , e.g. equilibrium conditions, are maintained.
Edit intent: fluency
Original:  In particular, we show how to construct the smallest and largest values of the counterfactual as the distribution of unobservables spans nonparametric neighborhoods of the researcher's assumed specification while other `structural' features of the model , e.g. equilibrium conditions, are maintained.
Revised:  In particular, we show how to construct the smallest and largest values of the counterfactual as the distribution of unobservables spans nonparametric neighborhoods of the researcher's assumed specification while other `structural' features of the model are maintained.
Edit intent: coherence
Original:  Our methods are computationally simple to implement, with the nuisance distribution effectively profiled out via a low-dimensional convex program . Our procedure delivers sharp bounds for the identified set of counterfactuals (i.e. without parametric assumptions about the distribution of unobservables) as the neighborhoods become large .
Revised:  Our procedure replaces the infinite-dimensional optimization with respect to the distribution by a finite-dimensional convex program and is therefore computationally simple to implement. We develop a novel MPEC implementation of our procedure to further simplify computation in models featuring endogenous parameters defined by equilibrium constraints. Our procedure recovers sharp bounds on the nonparametrically identified set of counterfactuals (i.e. without parametric assumptions about the distribution of unobservables) as the neighborhoods become large .
Edit intent: meaning-changed
Original:  Our procedure delivers sharp bounds for the identified set of counterfactuals (i.e. without parametric assumptions about the distribution of unobservables) as the neighborhoods become large . Over small neighborhoods , we relate our procedure to a measure of local sensitivity which is further characterized using an influence function representation. We provide a suitable sampling theory for plug-in estimators and apply our procedure to models of strategic interaction and dynamic discrete choice.
Revised:  Our procedure delivers sharp bounds for the identified set of counterfactuals over large neighborhoods and has connections with local approaches to sensitivity analysis over small neighborhoods. We propose plug-in estimators and apply our procedure to models of strategic interaction and dynamic discrete choice.
Edit intent: clarity
Original: The paper [S{\o}rensen et al., Nature 532] presents and discusses results for a Quantum Moves game , BringHomeWater , where players have attempted to move a quantum state from one position to another in a simulated optical tweezer and atoms setup.
Revised: The paper [S{\o}rensen et al., Nature 532] considers how human players compare to algorithms for solving the Quantum Moves game , BringHomeWater , where players have attempted to move a quantum state from one position to another in a simulated optical tweezer and atoms setup.
Edit intent: meaning-changed
Original: The paper [S{\o}rensen et al., Nature 532] presents and discusses results for a Quantum Moves game , BringHomeWater , where players have attempted to move a quantum state from one position to another in a simulated optical tweezer and atoms setup. The paper compares the player solutions to numerical methods that the authors discuss. In particular, [S{\o}rensen et al., Nature 532] show evidence that ] the so-called Krotov optimization method performs worse than solutions that the human players have come up with.
Revised: The paper [S{\o}rensen et al., Nature 532] presents and discusses results for a Quantum Moves game BringHomeWater and design new algorithms based on the intuition extracted from players. The claim by [S{\o}rensen et al., Nature 532] show evidence that ] the so-called Krotov optimization method performs worse than solutions that the human players have come up with.
Edit intent: coherence
Original:  In particular, [S{\o}rensen et al., Nature 532] show evidence that ] the so-called Krotov optimization method performs worse than solutions that the human players have come up with.
Revised:  In particular, [S{\o}rensen et al., Nature 532] is that players outperform widely used algorithms, in particular the KASS algorithm, based on the Krotov algorithm, and that player intuition is crucial to develop improved methods. However, as initially discussed by D. Sels ] the so-called Krotov optimization method performs worse than solutions that the human players have come up with.
Edit intent: meaning-changed
Original:  In particular, [S{\o}rensen et al., Nature 532] show evidence that ] the so-called Krotov optimization method performs worse than solutions that the human players have come up with.
Revised:  In particular, [S{\o}rensen et al., Nature 532] show evidence that D. Sels, Phys. Rev. A 97] the so-called Krotov optimization method performs worse than solutions that the human players have come up with.
Edit intent: meaning-changed
Original:  In particular, [S{\o}rensen et al., Nature 532] show evidence that ] the so-called Krotov optimization method performs worse than solutions that the human players have come up with.
Revised:  In particular, [S{\o}rensen et al., Nature 532] show evidence that ], a standard Coordinate Ascent algorithm outperforms all players by a large margin. Albeit D. Sels only compare to player solutions, the simple algorithm outperforms all algorithms based on player solutions and Krotov, and it does so using much less time and iterations. In this paper we elaborate on the methods discussed by D. Sels and verify that the so-called Krotov optimization method performs worse than solutions that the human players have come up with.
Edit intent: meaning-changed
Original:  In particular, [S{\o}rensen et al., Nature 532] show evidence that ] the so-called Krotov optimization method performs worse than solutions that the human players have come up with. Given the assumption that the Krotovmethod has been correctly applied, the evidence points to the fact that human players can outperform the Krotov method. This is the find and claim of [S{\o}rensen et al., Nature 532] and it features prominent in the abstract of the paper.
Revised:  In particular, [S{\o}rensen et al., Nature 532] show evidence that ] the presented algorithm, solves the problem better than all players and algorithms derived from player solutions in [S{\o}rensen et al., Nature 532] and it features prominent in the abstract of the paper.
Edit intent: meaning-changed
Original:  This is the find and claim of [S{\o}rensen et al., Nature 532] and it features prominent in the abstract of the paper. It leads the authors to conclude that 'playerssucceed where purely numerical optimization fails, and analysis of their solutions provide insights into the problem of optimization of a more profound and general nature. ' While it seems clear from the presented data that human players have indeed outperformed a particular implementation of the Krotov algorithm (and, according to [S{\o}rensen et al., Nature 532] , as a consequence also algorithms like CRAB and evolutionary approaches which perform worse than Krotov), there is no reason to believe that this finding is of any particular significance.
Revised:  This is the find and claim of [S{\o}rensen et al., Nature 532] . We also verify the theoretical analysis presented by D. Sels, that gives a theoretically derived protocol that outperforms all players. We add a comparison with gradient ascent or GRAPE. Starting from uniform random values, GRAPE outperforms all players by a large margin. GRAPE works at least as well as the methods from [S{\o}rensen et al., Nature 532] , as a consequence also algorithms like CRAB and evolutionary approaches which perform worse than Krotov), there is no reason to believe that this finding is of any particular significance.
Edit intent: meaning-changed
Original:  ' While it seems clear from the presented data that human players have indeed outperformed a particular implementation of the Krotov algorithm (and, according to [S{\o}rensen et al., Nature 532] , as a consequence also algorithms like CRAB and evolutionary approaches which perform worse than Krotov), there is no reason to believe that this finding is of any particular significance. In fact, as has been discussed first by D. Sels [ D.Sels, Phys.
Revised:  ' While it seems clear from the presented data that human players have indeed outperformed a particular implementation of the Krotov algorithm (and, according to [S{\o}rensen et al., Nature 532] initialized with player solutions. A standard analysis of the results from GRAPE provides a starting point for GRAPE, that outperform all algorithms from [ D.Sels, Phys.
Edit intent: meaning-changed
Original:  In fact, as has been discussed first by D. Sels [ D.Sels, Phys. Rev. A 97, 040302 , a very simple approach using classical arguments can capture the BringHomeWater Quantum Moves game far better than the player approach.
Revised:  In fact, as has been discussed first by D. Sels [ , a very simple approach using classical arguments can capture the BringHomeWater Quantum Moves game far better than the player approach.
Edit intent: coherence
Original: Consider a situation wherein a stream of individuals arrive sequentially - for example when they get unemployed - to a social planner. Once each individual arrives, the planner needs to decide instantaneously on an action or treatment assignment - for example offering job training - while taking into account various institutional constraints such as limited budget and capacity.
Revised: Devising guidance on how to assign individuals to treatment is an important goal of empirical research. In practice individuals often arrive sequentially, and the planner faces various constraints such as limited budget and capacity.
Edit intent: clarity
Original:  Once each individual arrives, the planner needs to decide instantaneously on an action or treatment assignment - for example offering job training - while taking into account various institutional constraints such as limited budget and capacity. In this paper, we show how one can use offline observational data to estimate an optimal policy rule that maximizes ex-ante expected welfare in this dynamic context.
Revised:  Once each individual arrives, the planner needs to decide instantaneously on an action or treatment assignment - for example offering job training - while taking into account various institutional constraints such as limited budget /capacity, or borrowing constraints, or the need to place people in a queue. For instance, a governmental body may receive a budget outlay at the beginning of an year, and it may need to decide how best to allocate resources within the year to individuals who arrive sequentially. In this and other examples involving inter-temporal trade-offs, previous work on devising optimal policy rules in a static context is either not applicable, or is sub-optimal. Here we show how one can use offline observational data to estimate an optimal policy rule that maximizes ex-ante expected welfare in this dynamic context.
Edit intent: meaning-changed
Original:  For each policy , we show that a Partial Differential Equation (PDE) characterizes the evolution of the value function under that policy.
Revised:  The problem is equivalent to one of optimal control under a constrained policy class, and we exploit recent developments in Reinforcement Learning (RL) to propose an algorithm to solve this. The algorithm is easily implementable and computationally efficient, with speedups achieved through multiple RL agents learning in parallel processes. We also characterize the statistical regret from using our estimated policy rule. To do this, we show that a Partial Differential Equation (PDE) characterizes the evolution of the value function under that policy.
Edit intent: meaning-changed
Original:  For each policy , we show that a Partial Differential Equation (PDE) characterizes the evolution of the value function under that policy. Using the data , one can write down a sample version of the PDE that provides estimates of these value functions.
Revised:  For each policy , we show that a Partial Differential Equation (PDE) characterizes the evolution of the value function under each policy. The data enables us to obtain a sample version of the PDE that provides estimates of these value functions.
Edit intent: clarity
Original:  We then propose a modified Reinforcement Learning algorithm to solve for the policy rule that achieves the best value in the pre-specified class. The algorithm is easily implementable and computationally efficient, with speedups achieved through multiple reinforcement learning agents simultaneously learning the problem in parallel processes. By exploiting the properties of the PDEs , we show that the average social welfare attained by the estimated policy rule converges at a n^{-1/2} rate to the maximum attainable within the specified class of policy functions ;
Revised:  The estimated policy rule is the one with the maximal estimated value function. Using the theory of viscosity solutions to PDEs we show that the average social welfare attained by the estimated policy rule converges at a n^{-1/2} rate to the maximum attainable within the specified class of policy functions ;
Edit intent: meaning-changed
Original:  By exploiting the properties of the PDEs , we show that the average social welfare attained by the estimated policy rule converges at a n^{-1/2} rate to the maximum attainable within the specified class of policy functions ;
Revised:  By exploiting the properties of the PDEs , we show that the policy regret decays at a n^{-1/2} rate to the maximum attainable within the specified class of policy functions ;
Edit intent: meaning-changed
Original:  By exploiting the properties of the PDEs , we show that the average social welfare attained by the estimated policy rule converges at a n^{-1/2} rate to the maximum attainable within the specified class of policy functions ;
Revised:  By exploiting the properties of the PDEs , we show that the average social welfare attained by the estimated policy rule converges at a n^{-1/2} rate in most examples ;
Edit intent: clarity
Original:  To do this, we show that a Partial Differential Equation (PDE) characterizes the evolution of the value function under each policy . The data enables us to obtain a sample version of the PDEthat provides estimates of these value functions. The estimated policy rule is the one with the maximal estimated value function. Using the theory of viscosity solutions to PDEs we show that the policy regret decays at a n^{-1/2} rate in most examples;
Revised:  To do this, we show that a Partial Differential Equation (PDE) characterizes the evolution of the value function under each policy in a Partial Differential Equation (PDE) form and using the theory of viscosity solutions to PDEs we show that the policy regret decays at a n^{-1/2} rate in most examples;
Edit intent: clarity
Original: In this paper, I show that a nonseparable model where the endogenous variable is multivalued can be point-identified even when the instrument (IV) is only binary. Though the order condition generally fails in this case, I show that exogenous covariates are able to generate enough moment equations to restore the order condition as if enlarging the IV's support under very general selection mechanisms for the endogenous variable.
Revised: In this paper, I show that a nonseparable model where the endogenous variable is multivalued can be point-identified even when the instrument takes on too few values. This paper presents a new method that matches pairs of covariates and instruments to restore point identification in this scenario in a triangular model. The model consists of a structural function for a continuous outcome and a selection model for the discrete endogenous variable.
Edit intent: meaning-changed
Original:  No restrictions are imposed on the way these covariates enter the model, such as separability or monotonicity. Further, after the order condition is fulfilled, I provide a new sufficient condition that is weaker than the existing results for the global uniqueness of the solution to the nonlinear system of equations.
Revised:  The structural outcome function must be continuous and monotonic in a scalar disturbance, but it can be nonseparable. The selection model allows for unrestricted heterogeneity. Global identification is obtained under weak conditions. The paper also provides estimators of the structural outcome function. Two empirical examples of the return to education and selection into Head Start illustrate the value and limitations of the nonlinear system of equations.
Edit intent: meaning-changed
Original:  Further, after the order condition is fulfilled, I provide a new sufficient condition that is weaker than the existing results for the global uniqueness of the solution to the nonlinear system of equations. Based on the identification result, I propose a sieves estimator and uniform consistency and pointwise asymptotic normality are established under simple low-level conditions. A Monte Carlo experiment is conducted to examine its performance .
Revised:  Further, after the order condition is fulfilled, I provide a new sufficient condition that is weaker than the existing results for the global uniqueness of the solution to the method .
Edit intent: clarity
Original:  As a result, we found that, even in the case with observation errors, the only strategy sets that enforce a liner payoff relationship are either ZD strategies or unconditional strategies  , which were numerically confirmed .
Revised:  As a result, we found that, even in the case with observation errors, the only strategy sets that enforce a linear payoff relationship are either ZD strategies or unconditional strategies , which were numerically confirmed .
Edit intent: fluency
Original:  As a result, we found that, even in the case with observation errors, the only strategy sets that enforce a liner payoff relationship are either ZD strategies or unconditional strategies  , which were numerically confirmed .
Revised:  As a result, we found that, even in the case with observation errors, the only strategy sets that enforce a liner payoff relationship are either ZD strategies or unconditional strategies and that no other strategies can enforce it , which were numerically confirmed .
Edit intent: meaning-changed
Original:  As a result, we found that, even in the case with observation errors, the only strategy sets that enforce a liner payoff relationship are either ZD strategies or unconditional strategies  , which were numerically confirmed . Moreover, we derived the feasible ranges of the expected payoffs for unconditional strategies .
Revised:  As a result, we found that, even in the case with observation errors, the only strategy sets that enforce a liner payoff relationship are either ZD strategies or unconditional strategies , which were numerically confirmed .
Edit intent: coherence
Original:  Here, we consider only simple weighted base pair counting  to obtain BPPart algorithm for Base-pair Partition function and BPMax for Base-pair Maximization, which use 9 and 2 tables respectively.
Revised:  Here, simple weighted base pair counting to obtain BPPart algorithm for Base-pair Partition function and BPMax for Base-pair Maximization, which use 9 and 2 tables respectively.
Edit intent: coherence