wanyu/IteraTeR_full_sent huggingface dataset:
Original: This paper proposes a method for estimating the causal effect of a discrete intervention in observational time-series using encoder-decoder networks . Encoder-decoder networks, which are special class of recurrent neural networks (RNNs) suitable for handling variable-length sequential data, are used to predict a counterfactual time-series of treated unit outcomes using only the pre-intervention outcomes of control units as inputs.
Revised: This paper proposes a method for estimating the causal effect of a discrete intervention in observational time-series using encoder-decoder networks . Encoder-decoder recurrent neural networks (RNNs) suitable for handling variable-length sequential data, are used to predict a counterfactual time-series of treated unit outcomes using only the pre-intervention outcomes of control units as inputs.
Edit intent: coherence
Original: This paper proposes a method for estimating the causal effect of a discrete intervention in observational time-series using encoder-decoder networks . Encoder-decoder networks, which are special class of recurrent neural networks (RNNs) suitable for handling variable-length sequential data, are used to predict a counterfactual time-series of treated unit outcomes using only the pre-intervention outcomes of control units as inputs.
Revised: This paper proposes a method for estimating the causal effect of a discrete intervention in observational time-series using encoder-decoder networks . Encoder-decoder networks, which are special class of recurrent neural networks (RNNs) are used to predict a counterfactual time-series of treated unit outcomes using only the pre-intervention outcomes of control units as inputs.
Edit intent: clarity
Original: This paper proposes a method for estimating the causal effect of a discrete intervention in observational time-series using encoder-decoder networks . Encoder-decoder networks, which are special class of recurrent neural networks (RNNs) suitable for handling variable-length sequential data, are used to predict a counterfactual time-series of treated unit outcomes using only the pre-intervention outcomes of control units as inputs.
Revised: This paper proposes a method for estimating the causal effect of a discrete intervention in observational time-series using encoder-decoder networks . Encoder-decoder networks, which are special class of recurrent neural networks (RNNs) suitable for handling variable-length sequential data, are used to predict counterfactual time-series of treated unit outcomes using only the pre-intervention outcomes of control units as inputs.
Edit intent: fluency
Original: This paper proposes a method for estimating the causal effect of a discrete intervention in observational time-series using encoder-decoder networks . Encoder-decoder networks, which are special class of recurrent neural networks (RNNs) suitable for handling variable-length sequential data, are used to predict a counterfactual time-series of treated unit outcomes using only the pre-intervention outcomes of control units as inputs.
Revised: This paper proposes a method for estimating the causal effect of a discrete intervention in observational time-series using encoder-decoder networks . Encoder-decoder networks, which are special class of recurrent neural networks (RNNs) suitable for handling variable-length sequential data, are used to predict a counterfactual time-series of treated unit outcomes using only the outcomes of control units as inputs.
Edit intent: clarity
Original:  Unlike SCM, the proposed method does not rely on pre-intervention covariates, allows for nonconvex combinations of control units, and can handle multiple treated units . In empirical and simulated data applications, RNN-based models outperform SCM in terms of predictive accuracy while using much less information to produce counterfactual predictions .
Revised:  Unlike SCM, the proposed method does not rely on pre-intervention covariates, allows for nonconvex combinations of control units, and can handle multiple treated units , and can share model parameters across time-steps. RNNs outperform SCM in terms of predictive accuracy while using much less information to produce counterfactual predictions .
Edit intent: meaning-changed
Original:  In empirical and simulated data applications, RNN-based models outperform SCM in terms of predictive accuracy while using much less information to produce counterfactual predictions .
Revised:  In empirical and simulated data applications, RNN-based models outperform SCM in terms of recovering experimental estimates from a field experiment extended to a time-series observational setting. In placebo tests run on three different benchmark datasets, RNNs are more accurate than SCM in predicting the post-intervention time-series of control units, while yielding a comparable proportion of false positives. The proposed method contributes to a new literature that uses machine learning techniques for data-driven counterfactual prediction .
Edit intent: meaning-changed
Original:  Recurrent neural networks (RNNs) are used to predict counterfactual time-series of treated unit outcomes using only the outcomes of control units as inputs. Unlike SCM, the proposed method does not rely on pre-intervention covariates , allows for nonconvex combinations of control units, can handle multiple treated units, and can share model parameters across time-steps.
Revised:  Recurrent neural networks (RNNs) are used to predict counterfactual time-series of treated unit outcomes using only the outcomes of control units as predictors. This approach is less susceptible to p-hacking because it does not require the researcher to choose predictors or pre-intervention covariates , allows for nonconvex combinations of control units, can handle multiple treated units, and can share model parameters across time-steps.
Edit intent: clarity
Original:  Unlike SCM, the proposed method does not rely on pre-intervention covariates , allows for nonconvex combinations of control units, can handle multiple treated units, and can share model parameters across time-steps.
Revised:  Unlike SCM, the proposed method does not rely on pre-intervention covariates to construct the synthetic control. RNNs do not assume a functional form, can learn nonconvex combinations of control units, can handle multiple treated units, and can share model parameters across time-steps.
Edit intent: meaning-changed
Original:  Unlike SCM, the proposed method does not rely on pre-intervention covariates , allows for nonconvex combinations of control units, can handle multiple treated units, and can share model parameters across time-steps. RNNs outperform SCM in terms of recovering experimental estimates from a field experiment extended to a time-series observational setting. In placebo tests run on three different benchmark datasets, RNNs are more accurate than SCM in predicting the post-intervention time-series of control units, while yielding a comparable proportion of false positives.The proposed method contributes to a new literature that uses machine learning techniques for data-driven counterfactual prediction .
Revised:  Unlike SCM, the proposed method does not rely on pre-intervention covariates , allows for nonconvex combinations of control units, and are specifically structured to exploit temporal dependencies in sequential data. I apply the approach to the problem of estimating the long-run impacts of U.S. homestead policy on public school spending .
Edit intent: meaning-changed
Original:  Recurrent neural networks (RNNs) are used to predict the counterfactual outcomes of treated units using only the outcomes of control units as predictors. This approach is less susceptible to p-hacking because it does not require the researcher to choose predictors or pre-intervention covariates to construct the synthetic control. RNNs do not assume a functional form, can learn nonconvex combinations of control units, and are specifically structured to exploit temporal dependencies in sequential data.
Revised:  Recurrent neural networks (RNNs) are used to predict the counterfactual outcomes of treated units using only the outcomes of control units is then applied to the treated units for predicting counterfactual outcomes. RNNs are specifically structured to exploit temporal dependencies in sequential data.
Edit intent: coherence
Original:  RNNs do not assume a functional form, can learn nonconvex combinations of control units, and are specifically structured to exploit temporal dependencies in sequential data. I apply the approach to the problem of estimating the long-run impacts of U.S. homestead policy on public school spending.
Revised:  RNNs do not assume a functional form, can learn nonconvex combinations of control units, and are specifically structured to exploit temporal dependencies in panel data, and are able to learn negative and nonlinear interactions between control unit outcomes. We apply the method to the problem of estimating the long-run impacts of U.S. homestead policy on public school spending.
Edit intent: meaning-changed
Original:  In this paper, we consider the energy consumption tradeoff among communication, computation, and caching (C3) for data analytics under a Quality of Information (QoI) guarantee in a geo-distributed system.
Revised:  In this paper, we consider the problem of energy consumption in data communication, compression and caching (C3) for data analytics under a Quality of Information (QoI) guarantee in a geo-distributed system.
Edit intent: clarity
Original:  In this paper, we consider the energy consumption tradeoff among communication, computation, and caching (C3) for data analytics under a Quality of Information (QoI) guarantee in a geo-distributed system. To attain this goal , we formulate an optimization problem to capture the C3 costs, which turns out to be a non-convex Mixed Integer Non-Linear Programming }  (MINLP) Problem.
Revised:  In this paper, we consider the energy consumption tradeoff among communication, computation, and caching (C3) for data analytics under a Quality of Information (QoI) guarantee in a communication network. } (MINLP) Problem.
Edit intent: clarity
Original:  To attain this goal , we formulate an optimization problem to capture the C3 costs, which turns out to be a non-convex Mixed Integer Non-Linear Programming }  (MINLP) Problem.
Revised:  To attain this goal , we formulate an optimization problem to capture the C3 costs, which turns out to be a non-convex Mixed Integer Non-Linear Programming Our goal is to identify the optimal data compression rate and data placement over the network to minimize the overall energy consumption in the network.} (MINLP) Problem.
Edit intent: meaning-changed
Original:  To attain this goal , we formulate an optimization problem to capture the C3 costs, which turns out to be a non-convex Mixed Integer Non-Linear Programming }  (MINLP) Problem.
Revised:  To attain this goal , we formulate an optimization problem to capture the C3 costs, which turns out to be a non-convex Mixed Integer Non-Linear Programming } he formulated problem is a (MINLP) Problem.
Edit intent: clarity
Original:  To attain this goal , we formulate an optimization problem to capture the C3 costs, which turns out to be a non-convex Mixed Integer Non-Linear Programming }  (MINLP) Problem.
Revised:  To attain this goal , we formulate an optimization problem to capture the C3 costs, which turns out to be a non-convex Mixed Integer Non-Linear Programming } Mixed Integer Non-Linear Programming (MINLP) Problem.
Edit intent: fluency
Original:  To attain this goal , we formulate an optimization problem to capture the C3 costs, which turns out to be a non-convex Mixed Integer Non-Linear Programming }  (MINLP) Problem. We then  propose a variant of spatial branch and bound algorithm (V-SBB), that can achieve e-global  optimal solution to the original MINLP.
Revised:  To attain this goal , we formulate an optimization problem to capture the C3 costs, which turns out to be a non-convex Mixed Integer Non-Linear Programming } (MINLP) problem with non-convex functions, which is NP-hard in general. propose a variant of spatial branch and bound algorithm (V-SBB), that can achieve e-global optimal solution to the original MINLP.
Edit intent: meaning-changed
Original:  We then  propose a variant of spatial branch and bound algorithm (V-SBB), that can achieve e-global  optimal solution to the original MINLP. We show numerically that V-SBB is more stable and robust than other candidate MINLP solvers under different network scenarios. More importantly, we observe that the energy efficiency under { our C3 optimization framework improves by as much as 88 \% compared to any C2 optimization between communication and computation or caching.
Revised:  We then propose a variant of spatial branch and bound algorithm (V-SBB), that can achieve e-global optimal solution to our problem { our C3 optimization framework improves by as much as 88 \% compared to any C2 optimization between communication and computation or caching.
Edit intent: clarity
Original:  More importantly, we observe that the energy efficiency under { our C3 optimization framework improves by as much as 88 \% compared to any C2 optimization between communication and computation or caching.
Revised:  More importantly, we observe that the energy efficiency under .{ our C3 optimization framework improves by as much as 88 \% compared to any C2 optimization between communication and computation or caching.
Edit intent: fluency
Original:  More importantly, we observe that the energy efficiency under { our C3 optimization framework improves by as much as 88 \% compared to any C2 optimization between communication and computation or caching.
Revised:  More importantly, we observe that the energy efficiency under {We numerically show that our C3 optimization framework improves by as much as 88 \% compared to any C2 optimization between communication and computation or caching.
Edit intent: meaning-changed
Original:  This condition precludes oscillations in the MM approximations of the processive and distributive systems, shown previously, as well as in the MM approximation of the partially processive and partially distributive mixed-mechanism system.
Revised:  This implies the lack of oscillations in the MM approximations of the processive and distributive systems, shown previously, as well as in the MM approximation of the partially processive and partially distributive mixed-mechanism system.
Edit intent: clarity
Original:  Currently, the wrong theoretical model gave about the five orders of magnitude less than facts , and became a big problem and obstacle for thermogenesis studies in the field of BA . Here, based on the first law of thermodynamics and thermal diffusion equation, the thermal physical model of thermogenesis for BA is deduced.
Revised:  Currently, the wrong theoretical model gave about the five orders of magnitude less than facts . Here, based on the first law of thermodynamics and thermal diffusion equation, the thermal physical model of thermogenesis for BA is deduced.
Edit intent: clarity
Original:  Currently, the wrong theoretical model gave about the five orders of magnitude less than facts , and became a big problem and obstacle for thermogenesis studies in the field of BA . Here, based on the first law of thermodynamics and thermal diffusion equation, the thermal physical model of thermogenesis for BA is deduced. We found the mitochondrial thermogenesis of brown adipocyte is a special case for thermal diffusion equation.
Revised:  Currently, the wrong theoretical model gave about the five orders of magnitude less than facts , and became a big problem and obstacle for thermogenesis studies in the field of BA . Here, based on the first law of thermodynamics and thermal diffusion equation, the deduced theoretical model of mitochondrial thermogenesis satisfies Laplace equation, and is a special case for thermal diffusion equation.
Edit intent: meaning-changed
Original:  We derive finite-sample optimal estimators and confidence intervals (CIs) under the assumption of normal errors when the conditional mean of the outcome variable is constrained only by nonparametric smoothness and/or shape restrictions .
Revised:  Given nonparametric smoothness and/or shape restrictions on the conditional mean of the outcome variable, we derive estimators and confidence intervals (CIs) under the assumption of normal errors when the conditional mean of the outcome variable is constrained only by nonparametric smoothness and/or shape restrictions .
Edit intent: clarity
Original:  We derive finite-sample optimal estimators and confidence intervals (CIs) under the assumption of normal errors when the conditional mean of the outcome variable is constrained only by nonparametric smoothness and/or shape restrictions .
Revised:  We derive finite-sample optimal estimators and confidence intervals (CIs) that are optimal in finite samples when the regression errors are normal with known variance. In contrast to conventional CIs, our CIs use a larger critical value that explicitly takes into account the potential bias of the estimator. When the error distribution is unknown, feasible versions of our CIs are valid asymptotically, even when \sqrt{n the conditional mean of the outcome variable is constrained only by nonparametric smoothness and/or shape restrictions .
Edit intent: meaning-changed
Original:  We derive finite-sample optimal estimators and confidence intervals (CIs) under the assumption of normal errors when the conditional mean of the outcome variable is constrained only by nonparametric smoothness and/or shape restrictions .
Revised:  We derive finite-sample optimal estimators and confidence intervals (CIs) under the assumption of normal errors when the conditional mean . We also derive the minimum smoothness conditions on the conditional mean that are necessary for \sqrt{n .
Edit intent: meaning-changed
Original:  When the conditional mean is restricted to be Lipschitz with a large enough bound on the Lipschitz constant, we show that the optimal estimator reduces to a matching estimator with the number of matches set to one.
Revised:  When the conditional mean is restricted to be Lipschitz with a large enough bound on the Lipschitz constant, the optimal estimator reduces to a matching estimator with the number of matches set to one.
Edit intent: coherence
Original:  In contrast to conventional CIs, our CIs use a larger critical value that explicitly takes into account the potential bias of the estimator. It is needed for correct coverage in finite samples and, in certain cases, asymptotically. We give conditions under which root-n inference is impossible, and we provide versions of our CIs that are feasible and asymptotically valid with unknown error distribution, including in this non-regular case. We apply our results in a numerical illustration and in an application to the National Supported Work Demonstration.
Revised:  We illustrate our methods in an application to the National Supported Work Demonstration.
Edit intent: coherence
Original:  Our approach is agnostic about the properties of the machine learning estimators used to produce proxies, and it completely avoids making any strong assumption.
Revised:  Our approach is generic, it can be used in conjunction with penalized methods, deep and shallow neural networks, canonical and new random forests, boosted trees, and ensemble methods. Our approach is agnostic and does not make unrealistic or hard-to-check assumptions; we don't require conditions for consistency of the ML methods.
Edit intent: meaning-changed
Original:  Our variational inferencemethod is shown to be uniformly valid and quantifies the uncertainty coming from both parameter estimation and data splitting. In essence , we take medians of p-values and medians of confidence intervals, resulting from many different data splits, and then adjust their nominal level to guarantee uniform validity.
Revised:  For inference , we take medians of p-values and medians of confidence intervals, resulting from many different data splits, and then adjust their nominal level to guarantee uniform validity.
Edit intent: clarity
Original:  An empirical application to the impact of micro-credit on economic development illustrates the use of the approach in randomized experiments . An additional application to the impact of the gender discrimination on wages illustrates the potential use of the approach in observational studies, where machine learning methods can be used to condition flexibly on very high-dimensional controls .
Revised:  An empirical application to the impact of micro-credit on economic development illustrates the use of the approach with two randomized experiments in development on the effects of microcredit and nudges to stimulate immunization demand .
Edit intent: meaning-changed
Original:  From a practical perspective, our method is flexible and easy to use: For both steps, we can use any loss-minimization method, e.g., penalized regression, deep neutral networks, or boosting;
Revised:  From a practical perspective, our method is flexible and easy to use: In both steps, we can use any loss-minimization method, e.g., penalized regression, deep neutral networks, or boosting;
Edit intent: clarity
Original:  Meanwhile, in the case of penalized kernel regression, we show that our method has a quasi-oracle property: Even if the pilot estimates for marginal effects and treatment propensities are not particularly accurate, we achieve the same regret bounds as an oracle who has a priori knowledge of these two nuisance components.
Revised:  Meanwhile, in the case of penalized kernel regression, we show that our method has a quasi-oracle property: Even if the pilot estimates for marginal effects and treatment propensities are not particularly accurate, we achieve the same error bounds as an oracle who has a priori knowledge of these two nuisance components.
Edit intent: fluency
Original:  From a practical perspective, our method is flexible and easy to use: In both steps, we can use any loss-minimization method, e.g., penalized regression, deep neutral networks, or boosting;
Revised:  From a practical perspective, our method is flexible and easy to use: In both steps, we can use any loss-minimization method, e.g., penalized regression, deep neural networks, or boosting;
Edit intent: clarity
Original:  Previously, O(n\log n) time algorithms were known which are based on the technique for computing the minimum weight k-link path given in \mbox{%DIFAUXCMD kgon94,kgon95soda  By applying the new technique proposed in Jin's recent work for the dual problem of computing the maximum area triangle inside a convex polygon, we solve the problem at hand in O(n) time, thus justify Jin's claim that his technique may find applications in other polygonal inclusion problems.
Revised:  Previously, O(n\log n) time algorithms were known which are based on the technique for computing the minimum weight k-link path given in A. Aggarwal et. al. DCG94; B. Schieber. SODA95 By applying the new technique proposed in Jin's recent work for the dual problem of computing the maximum area triangle inside a convex polygon, we solve the problem at hand in O(n) time, thus justify Jin's claim that his technique may find applications in other polygonal inclusion problems.
Edit intent: meaning-changed