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The Augmented Synthetic Control Method
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| Pengarang | : | Eli Ben Michael |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 116 (No. 536) |
| Halaman | : | 1789-1803 |
| Abstrak | : | The synthetic control method (SCM) is a popular approach for estimating the impact of a treatment on a single unit in panel data settings. The “synthetic control” is a weighted average of control units that balances the treated unit’s pretreatment outcomes and other covariates as closely as possible. A critical feature of the original proposal is to use SCM only when the fit on pretreatment outcomes is excellent. We propose Augmented SCM as an extension of SCM to settings where such pretreatment fit is infeasible. Analogous to bias correction for inexact matching, augmented SCM uses an outcome model to estimate the bias due to imperfect pretreatment fit and then de-biases the original SCM estimate. Our main proposal, which uses ridge regression as the outcome model, directly controls pretreatment fit while minimizing extrapolation from the convex hull. This estimator can also be expressed as a solution to a modified synthetic controls problem that allows negative weights on some donor units. We bound the estimation error of this approach under different data-generating processes, including a linear factor model, and show how regularization helps to avoid over-fitting to noise. We demonstrate gains from Augmented SCM with extensive simulation studies and apply this framework to estimate the impact of the 2012 Kansas tax cuts on economic growth. We implement the proposed method in the new augsynth R package. |
Hubungan antara Pengetahuan mengenai Pityriasis versicolor dan PHBS dengan Kejadian Pityriasis versicolor pada Santri Madrasah Tsanawiyah Pondok Pesantren X Kecamatan Mempawah Hilir
| Pengarang | : | Diana Natalia, Sari Rahmayanti, Riska Nazaria |
| Nama Majalah/Jurnal | : | CDK Cermin dunia kedokteran |
| Volume / Edisi | : | 45 (No. 1) |
| Halaman | : | 7-12 |
| Abstrak | : | Pityriasis versicolor is fungal skin infection caused by Malassezia, found in 20-25% human population, mostly in moist and high temperature area, such as in West Borneo with an average temperature 25,8-28,33°C and humidity 98%. This cross-sectional study analyzed the correlation between knowledge on pityriasis versicolor and clean and healthy behavior with pityriasis versicolor incidence among Madrasah Tsanawiyah (MTs) students in Islamic Boarding School X Subdistrict Mempawah Hilir, using an observational analytical method with a cross-sectional approach. A total of 139 students were included, 45 diagnosed with pityriasis versicolor. A proportion of 57,8% subjects had a good knowledge on pityriasis versicolor and 93,3% had a good clean and healthy behavior. No correlation between knowledge on pityriasis versicolor and clean and healthy behavior with pityriasis versicolor incidence among MTs students in Islamic Boarding School X Subdistrict Mempawah Hilir. Diana Natalia, Sari Rahmayanti, Riska Nazaria. Correlation between Knowledge on Pityriasis versicolor and Clean and Healthy Behavior with Pityriasis versicolor Incidence among Students in Islamic Boarding School X Mempawah Hilir |
Counterfactual Analysis With Artificial Controls: Inference, High Dimensions, and Nonstationarity
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 116 (No. 536) |
| Halaman | : | 1773-1788 |
| Abstrak | : | Recently, there has been growing interest in developing statistical tools to conduct counterfactual analysis with aggregate data when a single “treated” unit suffers an intervention, such as a policy change, and there is no obvious control group. Usually, the proposed methods are based on the construction of an artificial counterfactual from a pool of “untre ated” peers, organized in a panel data structure. In this article, we consider a general framework for counterfactual analysis for high-dimensional, nonstationary data with either deterministic and/or stochastic trends, which nests well-established methods, such as the synthetic control. We propose a resampling procedure to test intervention effects that does not rely on postintervention asymptotics and that can be used even if there is only a single observation after the intervention. A simulation study is provided as well as an empirical application. Supplementary materials for this article are available online. |
On the Properties of the Synthetic Control Estimator with Many Periods and Many Controls
| Pengarang | : | Bruno Ferman |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 116 (No. 536) |
| Halaman | : | 1764-1772 |
| Abstrak | : | We consider the asymptotic properties of the synthetic control (SC) estimator when both the number of pretreatment periods and control units are large. If potential outcomes follow a linear factor model, we provide conditions under which the SC unit asymptotically recovers the factor structure of the treated unit, even when the pretreatment fit is imperfect. This happens when there are weights diluted among an increasing number of control units such that a weighted average of the factor structure of the control units asymptotically reconstructs the factor structure of the treated unit. In this case, the SC estimator is asymptotically unbiased even when treatment assignment is correlated with time-varying unobservables. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement. |
Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 116 (No. 536) |
| Halaman | : | 1746-1763 |
| Abstrak | : | This article proposes an imputation procedure that uses the factors estimated from a tall block along with the re-rotated loadings estimated from a wide block to impute missing values in a panel of data. Assuming that a strong factor structure holds for the full panel of data and its sub-blocks, it is shown that the common component can be consistently estimated at four different rates of convergence without requiring regularization or iteration. An asymptotic analysis of the estimation error is obtained. An application of our analysis is estimation of counterfactuals when potential outcomes have a factor structure. We study the estimation of average and individual treatment effects on the treated and establish a normal distribution theory that can be useful for hypothesis testing. |
On Robustness of Principal Component Regression
| Pengarang | : | Anish Agarwal |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 116 (No. 536) |
| Halaman | : | 1731-1745 |
| Abstrak | : | Principal component regression (PCR) is a simple, but powerful and ubiquitously utilized method. Its effectiveness is well established when the covariates exhibit low-rank structure. However, its ability to handle settings with noisy, missing, and mixed-valued, that is, discrete and continuous, covariates is not understood and remains an important open challenge. As the main contribution of this work, we establish the robustness of PCR, without any change, in this respect and provide meaningful finite-sample analysis. To do so, we establish that PCR is equivalent to performing linear regression after preprocessing the covariate matrix via hard singular value thresholding (HSVT). As a result, in the context of counterfactual analysis using observational data, we show PCR is equivalent to the recently proposed robust variant of the synthetic control method, known as robust synthetic control (RSC). As an immediate consequence, we obtain finite-sample analysis of the RSC estimator that was previously absent. As an important contribution to the synthetic controls literature, we establish that an (approximate) linear synthetic control exists in the setting of a generalized factor model, or latent variable model; traditionally in the literature, the existence of a synthetic control needs to be assumed to exist as an axiom. We further discuss a surprising implication of the robustness property of PCR with respect to noise, that is, PCR can learn a good predictive model even if the covariates are tactfully transformed to preserve differential privacy. Finally, this work advances the state-of-the-art analysis for HSVT by establishing stronger guarantees with respect to the ????2,∞-norm rather than the Frobenius norm as is commonly done in the matrix estimation literature, which may be of interest in its own right. |
Matrix Completion Methods for Causal Panel Data Models
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 116 (No. 536) |
| Halaman | : | 1716-1730 |
| Abstrak | : | In this article, we study methods for estimating causal effects in settings with panel data, where some units are exposed to a treatment during some periods and the goal is estimating counterfactual (untreated) outcomes for the treated unit/period combinations. 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. This leads to a matrix that well-approximates the original (incomplete) matrix, but has lower complexity according to the nuclear norm for matrices. We generalize results from the matrix completion literature by allowing the patterns of missing data to have a time series dependency structure that is common in social science applications. We 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 solely 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). The proposed method outperforms unconfoundedness-based or synthetic control estimators in simulations based on real data. |
Antara Naturalisasi dan Nasionalisme
| Pengarang | : | Abielmona, Rami |
| Nama Majalah/Jurnal | : | Basis |
| Volume / Edisi | : | 74 (No. 07-08) |
| Halaman | : | 2-4 |
| Abstrak | : | Dalam beberapa dekade terakhir, sejumlah tim nasional sepak bola di berbagai negara mengikutsertakan pemain yang tidak lahir atau dibesarkan di negara yang mereka wakili. Fenomena yang dikenal sebagai bagian dari naturalisasi ini mengimplikasikan bahwa seorang pemain asing dapat memperoleh kewarganegaraan suatu negara, sedemikian rupa sehingga ia dapat bermain di tim nasional negara tersebut. |
All-or-none processes in learning and retention
| Pengarang | : | Estes, W. K. |
| Nama Majalah/Jurnal | : | American Psychologist |
| Volume / Edisi | : | 19-1 (No. 0) |
| Halaman | : | 16-25 |
| Abstrak | : | However much we might prefer things to be otherwise, learning, as measured by available techniques, appears sometimes to be an essentially continuous, sometimes a sharply discontinuous process. Thus in current research we need models suitable to represent both kinds of data, while we await further evidence to determine which aspect is the more fundamental and which the derivative… . While awaiting more definitive clues concerning underlying properties, I myself have come to operate on the working assumption that all instances of apparently incremental changes in behavioral dispositions during learning are simply cases of incomplete analysis. |
Helping healthcare teams save lives during COVID-19: Insights and countermeasures from team science.
| Pengarang | : | Traylor, Allison M.; Tannenbaum, Scott I.; Thomas, Eric J.; Salas, Eduardo. |
| Nama Majalah/Jurnal | : | American Psychologist |
| Volume / Edisi | : | 56-1 (No. 0) |
| Halaman | : | 1-13 |
| Abstrak | : | As the COVID-19 pandemic has ravaged the United States, health care teams are on the frontlines of this global crisis, often navigating harrowing conditions at work, such as a lack of personal protective equipment and staffing shortages, and distractions at home, including worries about elderly relatives or making childcare arrangements. While the nature and severity of stressors impacting health care teams are in many ways unprecedented, decades of psychological research exploring teamwork in extreme contexts can provide insights to understand and improve outcomes for teams in a crisis. This review highlights the psychological principles that apply to teams in a crisis and illustrates how psychologists can use this knowledge to improve teamwork for medical teams in the midst of the COVID-19 pandemic., The review also provides a glimpse toward the future, noting both how psychologists can help health care teams recover and rebound, as well as how additional research can improve psychologists’ understanding of teamwork in times of crisis. |