一種部分識別因果效應(yīng)的路徑抽樣方法 工具變量模型

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摘要翻譯：
在一般的工具變量模型中，部分辨識方法是標準點辨識方法的一種靈活和魯棒的替代方法。然而，這種靈活性是以“基數(shù)詛咒”為代價的：隨著支持內(nèi)源治療的點數(shù)的增加，對識別集的限制數(shù)量呈指數(shù)增長。本文提出了一種新的路徑采樣方法來應(yīng)對這一挑戰(zhàn)。它被設(shè)計為在最復(fù)雜的連續(xù)內(nèi)源性處理模型中部分識別感興趣的因果效應(yīng)。隨機過程表示允許無縫地將個人行為的假設(shè)納入模型。一些潛在的應(yīng)用包括不完全依從性隨機試驗中的劑量-反應(yīng)估計、社會計劃的評估、需求模型中的福利估計和連續(xù)選擇模型。作為一個證明，該方法在支出連續(xù)的假設(shè)下提供了家庭支出的信息性非參數(shù)界。數(shù)學貢獻是在路徑空間上通過采樣近似求解無窮維線性規(guī)劃的一種方法。
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英文標題：
《A path-sampling method to partially identify causal effects in
  instrumental variable models》
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作者：
Florian Gunsilius
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最新提交年份：
2020
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分類信息：

一級分類：Economics        經(jīng)濟學
二級分類：Econometrics        計量經(jīng)濟學
分類描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
計量經(jīng)濟學理論，微觀計量經(jīng)濟學，宏觀計量經(jīng)濟學，通過新方法發(fā)現(xiàn)的經(jīng)濟關(guān)系的實證內(nèi)容，統(tǒng)計推論應(yīng)用于經(jīng)濟數(shù)據(jù)的方法論方面。
--
一級分類：Mathematics        數(shù)學
二級分類：Statistics Theory        統(tǒng)計理論
分類描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
應(yīng)用統(tǒng)計、計算統(tǒng)計和理論統(tǒng)計：例如統(tǒng)計推斷、回歸、時間序列、多元分析、數(shù)據(jù)分析、馬爾可夫鏈蒙特卡羅、實驗設(shè)計、案例研究
--
一級分類：Statistics        統(tǒng)計學
二級分類：Statistics Theory        統(tǒng)計理論
分類描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的別名。漸近，貝葉斯推論，決策理論，估計，基礎(chǔ)，推論，檢驗。
--

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英文摘要：
  Partial identification approaches are a flexible and robust alternative to standard point-identification approaches in general instrumental variable models. However, this flexibility comes at the cost of a ``curse of cardinality'': the number of restrictions on the identified set grows exponentially with the number of points in the support of the endogenous treatment. This article proposes a novel path-sampling approach to this challenge. It is designed for partially identifying causal effects of interest in the most complex models with continuous endogenous treatments. A stochastic process representation allows to seamlessly incorporate assumptions on individual behavior into the model. Some potential applications include dose-response estimation in randomized trials with imperfect compliance, the evaluation of social programs, welfare estimation in demand models, and continuous choice models. As a demonstration, the method provides informative nonparametric bounds on household expenditures under the assumption that expenditure is continuous. The mathematical contribution is an approach to approximately solving infinite dimensional linear programs on path spaces via sampling.
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PDF鏈接：
https://arxiv.org/pdf/1910.09502

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關(guān)鍵詞：抽樣方法 工具變量 econometrics instrumental Expenditures 代價 continuous sampling partially path