wanyu/IteraTeR_full_sent huggingface dataset:
Original: In this paper we study methods for estimating causal effects in settings with panel data, where a subset of units are exposed to a treatment during a subset of periods , and the goal is estimating counterfactual (untreated) outcomes for the treated unit/period combinations.
Revised: In this paper we study methods for estimating causal effects in settings with panel data, where a subset of units are exposed to a treatment during some periods and the goal is estimating counterfactual (untreated) outcomes for the treated unit/period combinations.
Edit intent: clarity
Original:  We develop a class of matrix completion estimators that uses the observed elements of the matrix of control outcomes corresponding to untreated unit/periods to predict the "missing" elements of the  matrix, corresponding to treated units/periods.
Revised:  We develop a class of matrix completion estimators that uses the observed elements of the matrix of control outcomes corresponding to untreated unit/periods to impute the "missing" elements of the matrix, corresponding to treated units/periods.
Edit intent: clarity
Original:  We develop a class of matrix completion estimators that uses the observed elements of the matrix of control outcomes corresponding to untreated unit/periods to predict the "missing" elements of the  matrix, corresponding to treated units/periods.
Revised:  We develop a class of matrix completion estimators that uses the observed elements of the matrix of control outcomes corresponding to untreated unit/periods to predict the "missing" elements of the control outcome matrix, corresponding to treated units/periods.
Edit intent: fluency
Original:  We also present novel insights concerning the connections between the matrix completion literature, the literature on interactive fixed effects models and the literatures on program evaluation under unconfoundedness and synthetic control methods  .
Revised:  We also present novel insights concerning the connections between the matrix completion literature, the literature on interactive fixed effects models and the literatures on program evaluation under unconfoundedness and synthetic control methods . We show that all these estimators can be viewed as focusing on the same objective function. They differ in the way they deal with lack of identification, in some cases solely through regularization (our proposed nuclear norm matrix completion estimator) and in other cases primarily through imposing hard restrictions (the unconfoundedness and synthetic control approaches). proposed method outperforms unconfoundedness-based or synthetic control estimators .
Edit intent: meaning-changed
Original:  We develop a class of matrix completion estimators that uses the observed elements of the matrix of control outcomes corresponding to untreated unit/periods to impute the "missing" elements of the control outcome matrix, corresponding to treated units/periods.
Revised:  We propose a class of matrix completion estimators that uses the observed elements of the matrix of control outcomes corresponding to untreated unit/periods to impute the "missing" elements of the control outcome matrix, corresponding to treated units/periods.
Edit intent: clarity
Original:  They differ  in the way they deal with lack of identification, in some cases solely through regularization (our proposed nuclear norm matrix completion estimator) and in other cases primarily through imposing hard restrictions (the unconfoundedness and synthetic control approaches).  proposed method outperforms unconfoundedness-based or synthetic control estimators  .
Revised:  They differ solely in the way they deal with lack of identification, in some cases solely through regularization (our proposed nuclear norm matrix completion estimator) and in other cases primarily through imposing hard restrictions (the unconfoundedness and synthetic control approaches). proposed method outperforms unconfoundedness-based or synthetic control estimators .
Edit intent: fluency
Original:  They differ  in the way they deal with lack of identification, in some cases solely through regularization (our proposed nuclear norm matrix completion estimator) and in other cases primarily through imposing hard restrictions (the unconfoundedness and synthetic control approaches).  proposed method outperforms unconfoundedness-based or synthetic control estimators  .
Revised:  They differ in the way they deal with identification, in some cases solely through regularization (our proposed nuclear norm matrix completion estimator) and in other cases primarily through imposing hard restrictions (the unconfoundedness and synthetic control approaches). proposed method outperforms unconfoundedness-based or synthetic control estimators .
Edit intent: clarity
Original:  They differ  in the way they deal with lack of identification, in some cases solely through regularization (our proposed nuclear norm matrix completion estimator) and in other cases primarily through imposing hard restrictions (the unconfoundedness and synthetic control approaches).  proposed method outperforms unconfoundedness-based or synthetic control estimators  .
Revised:  They differ in the way they deal with lack of identification, in some cases solely through regularization (our proposed nuclear norm matrix completion estimator) and in other cases primarily through imposing hard restrictions (the unconfoundedness and synthetic control approaches). The proposed method outperforms unconfoundedness-based or synthetic control estimators .
Edit intent: fluency
Original:  They differ  in the way they deal with lack of identification, in some cases solely through regularization (our proposed nuclear norm matrix completion estimator) and in other cases primarily through imposing hard restrictions (the unconfoundedness and synthetic control approaches).  proposed method outperforms unconfoundedness-based or synthetic control estimators  .
Revised:  They differ in the way they deal with lack of identification, in some cases solely through regularization (our proposed nuclear norm matrix completion estimator) and in other cases primarily through imposing hard restrictions (the unconfoundedness and synthetic control approaches). proposed method outperforms unconfoundedness-based or synthetic control estimators in simulations based on real data .
Edit intent: meaning-changed
Original:  We prove four main results for the case when all edges of both curves are long compared to the Fr\'echet distance between them: (1) a linear-time algorithm for deciding the Fr\'echet distance between two curves, (2) an algorithm that computes the Fr\'echet distance \Frd in O((n+m)\log (n+m)) time, (3) a linear-time d -
Revised:  We prove four results for the case when all edges of both curves are long compared to the Fr\'echet distance between them: (1) a linear-time algorithm for deciding the Fr\'echet distance between two curves, (2) an algorithm that computes the Fr\'echet distance \Frd in O((n+m)\log (n+m)) time, (3) a linear-time d -
Edit intent: clarity
Original:  We prove four main results for the case when all edges of both curves are long compared to the Fr\'echet distance between them: (1) a linear-time algorithm for deciding the Fr\'echet distance between two curves, (2) an algorithm that computes the Fr\'echet distance \Frd in O((n+m)\log (n+m)) time, (3) a linear-time d -
Revised:  We prove four main results for the case when all edges of both curves are long compared to the Fr\'echet distance between them: (1) a linear-time algorithm for deciding the Fr\'echet distance between two curves, (2) an algorithm that computes the \Frd in O((n+m)\log (n+m)) time, (3) a linear-time d -
Edit intent: fluency
Original:  We prove four main results for the case when all edges of both curves are long compared to the Fr\'echet distance between them: (1) a linear-time algorithm for deciding the Fr\'echet distance between two curves, (2) an algorithm that computes the Fr\'echet distance \Frd in O((n+m)\log (n+m)) time, (3) a linear-time d -
Revised:  We prove four main results for the case when all edges of both curves are long compared to the Fr\'echet distance between them: (1) a linear-time algorithm for deciding the Fr\'echet distance between two curves, (2) an algorithm that computes the Fr\'echet distance \Frd\ in O((n+m)\log (n+m)) time, (3) a linear-time d -
Edit intent: fluency
Original:  (1) a linear-time algorithm for deciding the Fr\'echet distance between two curves, (2) an algorithm that computes the %DIFDELCMD < \Frd%%% \ in O((n+m)\log (n+m)) time, (3) a linear-time d-approximation algorithm, and (4) a data structure that supports O(m\log^2 n)-time decision queries, where m is the number of vertices of the query curve and n the number of vertices of the preprocessed curve.
Revised:  (1) a linear-time algorithm for deciding the Fr\'echet distance between two curves, (2) an algorithm that computes the %DIFDELCMD < \Frd%%% Fr\'echet distance in O((n+m)\log (n+m)) time, (3) a linear-time d-approximation algorithm, and (4) a data structure that supports O(m\log^2 n)-time decision queries, where m is the number of vertices of the query curve and n the number of vertices of the preprocessed curve.
Edit intent: fluency
Original:  It is assumed in the present paper that the function \sigma satisfies a relatively mild condition expressed in terms of its local modulus of continuity in L^2, while the process B is a Volterra type Gaussian process.
Revised:  We assume that the function \sigma satisfies a relatively mild condition expressed in terms of its local modulus of continuity in L^2, while the process B is a Volterra type Gaussian process.
Edit intent: clarity
Original:  In the present paper, we establish a small-noise large deviation principle for the log-price in a fractional stochastic volatility model , and under an additional assumption of the self-similarity of the process B, derive a similar large deviation principle in the small-time regime.
Revised:  We establish a small-noise large deviation principle for the log-price in a fractional stochastic volatility model , and under an additional assumption of the self-similarity of the process B, derive a similar large deviation principle in the small-time regime.
Edit intent: clarity
Original:  In the present paper, we establish a small-noise large deviation principle for the log-price in a fractional stochastic volatility model , and under an additional assumption of the self-similarity of the process B, derive a similar large deviation principle in the small-time regime.
Revised:  In the present paper, we establish a small-noise large deviation principle for the log-price , and under an additional assumption of the self-similarity of the process B, derive a similar large deviation principle in the small-time regime.
Edit intent: clarity
Original:  The main results obtained in the present paper are generalizations of large deviation principles for the log-price process due to Forde and Zhang. We assume that the function \sigma satisfies a relatively mild condition expressed in terms of its local modulus of continuity in L^2, while the process is a Volterra type Gaussian process.
Revised:  The main results obtained in the present paper are generalizations of large deviation principles for the log-price process is a Volterra type Gaussian process.
Edit intent: coherence
Original:  We assume that the function \sigma satisfies a relatively mild condition expressed in terms of its local modulus of continuity in L^2, while the process is a Volterra type Gaussian process. The assumptions used by Forde and Zhang are more restrictive. It is supposed in their work that the function \sigma satisfies a global H\"{o and the process B is fractional Brownian motion.
Revised:  We assume that the function \sigma satisfies a relatively mild condition expressed in terms of its local modulus of continuity in L^2, while the process in such a model under the assumptions that the function \sigma satisfies a global H\"{o and the process B is fractional Brownian motion.
Edit intent: clarity
Original:  We establish a small-noise large deviation principle for the log-price, and under \widehat{B}  an additional assumption of the self-similarity of the process \widehat{B}, derive a similar large deviation principle in the small-time regime.
Revised:  In the present paper, we prove a similar small-noise large deviation principle for the log-price, and under \widehat{B} an additional assumption of the self-similarity of the process \widehat{B}, derive a similar large deviation principle in the small-time regime.
Edit intent: clarity
Original:  We establish a small-noise large deviation principle for the log-price, and under \widehat{B}  an additional assumption of the self-similarity of the process \widehat{B}, derive a similar large deviation principle in the small-time regime.
Revised:  We establish a small-noise large deviation principle under weaker restrictions on \sigma and \widehat{B} an additional assumption of the self-similarity of the process \widehat{B}, derive a similar large deviation principle in the small-time regime.
Edit intent: meaning-changed
Original:  We establish a small-noise large deviation principle for the log-price, and under \widehat{B}  an additional assumption of the self-similarity of the process \widehat{B}, derive a similar large deviation principle in the small-time regime.
Revised:  We establish a small-noise large deviation principle for the log-price, and under . We assume that \sigma satisfies rather mild local regularity and growth conditions, while the process\widehat{B} an additional assumption of the self-similarity of the process \widehat{B}, derive a similar large deviation principle in the small-time regime.
Edit intent: meaning-changed
Original:  We establish a small-noise large deviation principle for the log-price, and under \widehat{B}  an additional assumption of the self-similarity of the process \widehat{B}, derive a similar large deviation principle in the small-time regime.
Revised:  We establish a small-noise large deviation principle for the log-price, and under \widehat{B} is a Volterra type Gaussian process. Under an additional assumption of the self-similarity of the process \widehat{B}, derive a similar large deviation principle in the small-time regime.
Edit intent: meaning-changed
Original:  We establish a small-noise large deviation principle for the log-price, and under \widehat{B}  an additional assumption of the self-similarity of the process \widehat{B}, derive a similar large deviation principle in the small-time regime.
Revised:  We establish a small-noise large deviation principle for the log-price, and under \widehat{B} an additional assumption of the self-similarity of the process \widehat{B}, we derive a large deviation principle in the small-time regime.
Edit intent: clarity
Original:  This paper studies the machine-learning algorithms for developing the game AIs , and provides their structural interpretations. Specifically, chess-playing Deep Blue is a calibrated value function, whereas shogi-playing Bonanza represents an estimated value function via Rust's (1987) nested fixed-point method.
Revised:  This paper studies the machine-learning algorithms to develop AIs and the econometrics of dynamic structural models through the case studies of three famous game AIs. Chess-playing Deep Blue is a calibrated value function, whereas shogi-playing Bonanza represents an estimated value function via Rust's (1987) nested fixed-point method.
Edit intent: meaning-changed
Original:  its "reinforcement-learning value network" is equivalent to Hotz, Miller, Sanders, and Smith's (1994) simulation method for estimating the value function. Their performances suggest DNNs are a useful functional form when the state space is large and data are sparse. Explicitly incorporating strategic interactions and unobserved heterogeneity in the data-generating process would further improve AIs' explicability .
Revised:  its "reinforcement-learning value network" is equivalent to Hotz, Miller, Sanders, and Smith's (1994) conditional choice simulation method. Relaxing these AIs' implicit econometric assumptions would improve their structural interpretability .
Edit intent: clarity
Original: We document the initial-density dependence of the growth rate achieved by Jurkat cell cultures in a standard growth medium with fixed carrying capacity. As the density N_0 of the inoculum varies over 4 orders of magnitude, three distinct growth regimes appear. At small N_0, the growth rate \lambda is roughly constant and displays small sample-to-sample variability.
Revised: Individual cells exhibit specific proliferative responses to changes in microenvironmental conditions. Whether such potential is constrained by the cell density throughout the growth rate \lambda is roughly constant and displays small sample-to-sample variability.
Edit intent: clarity
Original:  At small N_0, the growth rate \lambda is roughly constant and displays small sample-to-sample variability. Upon increasing N_0, \lambda increasesslowly together with fluctuations. Finally, after peaking at an intermediate N_0, it gets smaller and more homogeneous across cultures as the inoculum density converges to the carrying capacity of the medium. A minimal, deterministic, population-based mathematical modelexplains the observed qualitative features assuming (in agreement with data) the presence of a weak positive correlation between cell density and proliferation rate .
Revised:  At small N_0, the growth process is however unclear. Here, we identify a theoretical framework that captures how the information encoded in the initial density of cancer cell populations impacts their growth profile. By following the growth of hundreds of populations of cancer cells, we found that the time they need to adapt to the environment decreases as the initial cell density increases .
Edit intent: clarity
Original:  These results support the idea that cell cultures can preserve a memory of the initial condition, possibly via contact interactions or through metabolic coupling .
Revised:  Moreover, the population growth rate shows a maximum at intermediate initial densities. With the support of a mathematical model, we show that the observed interdependence of adaptation time and growth rate is significantly at odds both with standard logistic growth models and with the Monod-like function that governs the dependence of the growth rate on nutrient levels. Our results (i) uncover and quantify a previously unnoticed heterogeneity in the growth dynamics of cancer cell populations; (ii) unveil how population growth may be affected by single-cell adaptation times; (iii) contribute to our understanding of the clinically-observed dependence of the primary and metastatic tumor take rates on the initial density of implanted cancer cells .
Edit intent: meaning-changed
Original:  To allow the insurance company's survival after ruin, the reinsurer would have to inject capital . Assuming that these capital injections are not from the shareholders , but rather obtained through such new reinsurance agreements, the problem here is to determine adequate reinsurance premiums.
Revised:  To allow the insurance company's survival after ruin, the reinsurer injects capital only at ruin times caused by jumps larger than a chosen retention level. Otherwise capital must be raised from the shareholders , but rather obtained through such new reinsurance agreements, the problem here is to determine adequate reinsurance premiums.
Edit intent: meaning-changed
Original:  Inspired by the results of Huzak et al. (2004) and Ben Salah (2014) on successive ruin events, we show that an explicit formula for this reinsurance premium exists in a setting where aggregate claims are modeled by a subordinator and a Brownian perturbation.
Revised:  Inspired by the results of Huzak et al. (2004) and Ben Salah (2014) on successive ruin events, we show that an explicit formula for these reinsurance premiums exists in a setting where aggregate claims are modeled by a subordinator and a Brownian perturbation.
Edit intent: fluency
Original:  Departing from the popular practice of restricting the norm of unknown parameters to be unity, we instead impose scale normalization on an arbitrary interquantile range, which proves particularly convenient for characterizing the identification relationships in our model, as quantiles provide direct linkages between the observable conditional probabilities and the unknown index values.
Revised:  We propose a novel form of scale normalization on an arbitrary interquantile range, which proves particularly convenient for characterizing the identification relationships in our model, as quantiles provide direct linkages between the observable conditional probabilities and the unknown index values.
Edit intent: clarity
Original:  We then use an inductive "in-fill " and " out-expansion" algorithm to establish our main identification results. We also provide a formal analysis of normalization that is without loss of generality in a precise sense, and discuss its implications concerning the interpretation of the results and counterfactual analyses of the model .
Revised:  We then use an inductive "in-fill " and " out-expansion" algorithm to establish our main results, and consider extensions to more general settings that allow nonseparable dependence between homophily and degree heterogeneity .
Edit intent: meaning-changed
Original:  We provide nonparametric identification results in a single large network setting for the potentially nonparametric homophily effect function, the  unobserved individual fixed effects and the unknown distribution of idiosyncratic pairwise shocks, up to normalization  for each possible true value of the unknown parameters.
Revised:  We provide nonparametric identification results in a single large network setting for the potentially nonparametric homophily effect function, the realizations of unobserved individual fixed effects and the unknown distribution of idiosyncratic pairwise shocks, up to normalization for each possible true value of the unknown parameters.
Edit intent: meaning-changed
Original:  We provide nonparametric identification results in a single large network setting for the potentially nonparametric homophily effect function, the  unobserved individual fixed effects and the unknown distribution of idiosyncratic pairwise shocks, up to normalization  for each possible true value of the unknown parameters.
Revised:  We provide nonparametric identification results in a single large network setting for the potentially nonparametric homophily effect function, the unobserved individual fixed effects and the unknown distribution of idiosyncratic pairwise shocks, up to normalization , for each possible true value of the unknown parameters.
Edit intent: fluency
Original:  We propose a novel form of scale normalization on an arbitrary interquantile range, which  proves particularly convenient for characterizing the identification relationships in our model , as quantiles provide direct linkages between the observable conditional probabilities and the unknown index values.
Revised:  We propose a novel form of scale normalization on an arbitrary interquantile range, which is not only theoretically robust but also proves particularly convenient for characterizing the identification relationships in our model , as quantiles provide direct linkages between the observable conditional probabilities and the unknown index values.
Edit intent: meaning-changed
Original:  We propose a novel form of scale normalization on an arbitrary interquantile range, which  proves particularly convenient for characterizing the identification relationships in our model , as quantiles provide direct linkages between the observable conditional probabilities and the unknown index values.
Revised:  We propose a novel form of scale normalization on an arbitrary interquantile range, which proves particularly convenient for the identification analysis , as quantiles provide direct linkages between the observable conditional probabilities and the unknown index values.
Edit intent: clarity