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Haavelmo 对联立方程估计的贡献AI 解读

This paper surveys Trygve Haavelmo’s contributions to econometrics. A brief summary of his 1944 monograph is followed by an analysis of the six important papers he contributed during 1943–47. Four of them were devoted to macroeconomic models including estimation of the Keynesian marginal propensity to consume; the first two introduced the methodology of a system of structural equations; the third marked a milestone in econometric method by deriving the reduced form of such a…

Summary Many empirical applications of regression discontinuity (RD) models use a running variable that is rounded and hence discrete, e.g. age in years, or birth weight in ounces. This paper shows that standard RD estimation using a rounded discrete running variable leads to inconsistent estimates of treatment effects, even when the true functional form relating the outcome and the running variable is known and is correctly specified. This paper provides simple formulas to…

删失数据下个体化治疗规则估计的双重稳健学习AI 解读

Individualized treatment rules recommend treatments based on individual patient characteristics in order to maximize clinical benefit. When the clinical outcome of interest is survival time, estimation is often complicated by censoring. We develop nonparametric methods for estimating an optimal individualized treatment rule in the presence of censored data. To adjust for censoring, we propose a doubly robust estimator which requires correct specification of either the…

Summary We consider estimation of the causal effect of a binary treatment on an outcome, conditionally on covariates, from observational studies or natural experiments in which there is a binary instrument for treatment. We describe a doubly robust, locally efficient estimator of the parameters indexing a model for the local average treatment effect conditionally on covariates V when randomization of the instrument is only true conditionally on a high dimensional vector of…

完全依从或完全不依从生存研究中的因果推断：半参数变换模型AI 解读

Summary We consider causal inference in randomized survival studies with right-censored outcomes and all-or-nothing compliance, using semiparametric transformation models to estimate the distribution of survival times in treatment and control groups, conditionally on covariates and latent compliance type. Estimands depending on these distributions, e.g. the complier average causal effect, the complier effect on survival beyond time t and the complier quantile effect, are…

非经典测量误差下条件矩模型的半参数估计AI 解读

This paper develops a framework for the analysis of semiparametric conditional moment models with endogenous and mismeasured causes, which is of empirical importance. We show that one set of valid instruments is sufficient to control for both endogeneity and measurement errors of the causes of interest, which has been observed in linear parametric models. Two-step consistent estimators of the parameters of interest are proposed. We also show that the proposed estimators are…

基于固定平滑渐近与埃奇沃思展开的分位数推断改进AI 解读

To estimate a sample quantile’s variance, the quantile spacing method involves smoothing parameter m. When m,n→∞, the corresponding Studentized test statistic is asymptotically N(0,1). Holding m fixed instead, the asymptotic distribution contains the Edgeworth expansion term capturing the variance of the quantile spacing. Consequently, the fixed-m distribution is more accurate than the standard normal under both asymptotic frameworks. A testing-optimal m is proposed to…

不同收入与选择过程下匹配和双重差分的偏差分析AI 解读

Matching and Difference in Difference (DID) are two widespread methods that use pre-treatment outcomes to correct for selection bias. I detail the sources of bias of both estimators in a model of earnings dynamics and entry into a Job Training Program (JTP) and I assess their performances using Monte Carlo simulations of the model calibrated with realistic parameter values. I find that Matching generally underestimates the average causal effect of the program and gets closer…

社会互动下的处理响应：通过单调比较静态实现部分识别AI 解读

This paper studies (nonparametric) partial identification of treatment response with social interactions. It imposes conditions motivated by economic theory on the primitives of the model, that is, the structural equations, and shows that they imply shape restrictions on the distribution of potential outcomes via monotone comparative statics. The econometric framework is tractable and allows for counterfactual predictions in models with multiple equilibria. Under three sets…

税收与公司政策：准自然实验的证据AI 解读

ABSTRACT We document important interactions between tax incentives and corporate policies using a “quasi natural experiment” provided by a surprise announcement that imposed corporate taxes on a group of Canadian publicly traded firms. The announcement caused a dramatic decrease in value. Prospective tax shields partially offset the losses, adding 4.6% to firm value on average, and vary with the tax status of the marginal investor. Further, firms adjust leverage, payout,…

Haavelmo 与因果演算的形成AI 解读

Haavelmo was the first to recognize the capacity of economic models to guide policies. This paper describes some of the barriers that Haavelmo’s ideas have had (and still have) to overcome and lays out a logical framework that has evolved from Haavelmo’s insight and matured into a coherent and comprehensive account of the relationships between theory, data, and policy questions. The mathematical tools that emerge from this framework now enable investigators to answer complex…

Haavelmo 之后的因果分析AI 解读

Haavelmo’s seminal 1943 and 1944 papers are the first rigorous treatment of causality. In them, he distinguished the definition of causal parameters from their identification. He showed that causal parameters are defined using hypothetical models that assign variation to some of the inputs determining outcomes while holding all other inputs fixed. He thus formalized and made operational Marshall’s (1890) ceteris paribus analysis. We embed Haavelmo’s framework into the…

高维工具变量回归的正则化方法及遗传基因组学应用AI 解读

In genetical genomics studies, it is important to jointly analyze gene expression data and genetic variants in exploring their associations with complex traits, where the dimensionality of gene expressions and genetic variants can both be much larger than the sample size. Motivated by such modern applications, we consider the problem of variable selection and estimation in high-dimensional sparse instrumental variables models. To overcome the difficulty of high…

兼顾效率与伦理的协变量调整响应自适应设计统一族AI 解读

Response-adaptive designs have recently attracted more and more attention in the literature because of its advantages in efficiency and medical ethics. To develop personalized medicine, covariate information plays an important role in both design and analysis of clinical trials. A challenge is how to incorporate covariate information in response-adaptive designs while considering issues of both efficiency and medical ethics. To address this problem, we propose a new and…

结构化 logistic–normal 混合模型的亚组分析推断AI 解读

In this article, we propose a statistical model for the purpose of identifying a subgroup that has an enhanced treatment effect as well as the variables that are predictive of the subgroup membership. The need for such subgroup identification arises in clinical trials and in market segmentation analysis. By using a structured logistic-normal mixture model, our proposed framework enables us to perform a confirmatory statistical test for the existence of subgroups, and at the…

联合观测与干预数据估计有向无环图的干预马尔可夫等价类AI 解读

Summary In many applications we have both observational and (randomized) interventional data. We propose a Gaussian likelihood framework for joint modelling of such different data types, based on global parameters consisting of a directed acyclic graph and corresponding edge weights and error variances. Thanks to the global nature of the parameters, maximum likelihood estimation is reasonable with only one or few data points per intervention. We prove consistency of the…

毒品使用是否存在递进效应：区分状态依赖与未观测异质性AI 解读

Empirically, teenagers who use soft drugs are more likely to use hard drugs in the future. This pattern can be explained by a causal effect (i.e., state dependence between drugs or stepping-stone effects) or by unobserved characteristics that make people more likely to use both soft and hard drugs (i.e., correlated unobserved heterogeneity). I estimate a dynamic discrete choice model of alcohol, marijuana and hard drug use over multiple years, and separately identify the…

广义回归折点设计中的因果效应推断AI 解读

A.1. Proofs of Propositions 1 and 2IN ORDER TO PROVE PROPOSITION 1, we first present and prove the following lemmas.LEMMA 1: Let ϕ(x t) : I × [c d] → R, where I is a compact subset of R m .Suppose ϕ(x t) and its partial derivative, ϕ 2 (x t), are continuous and that ϕ is integrable with respect to the probability measure α for each t.Then f (t) = ϕ(x t) dα(x) is continuously differentiable on [c d].PROOF: By Theorem 5 (p.97) of Roussas (2004), f (t) = ϕ 2 (x t) dα(x) for all…

工具变量有效性检验AI 解读

This paper develops a specification test for instrument validity in the heterogeneous treatment effect model with a binary treatment and a discrete instrument. The strongest testable implication for instrument validity is given by the condition for nonnegativity of point‐identifiable compliers' outcome densities. Our specification test infers this testable implication using a variance‐weighted Kolmogorov–Smirnov test statistic. The test can be applied to both discrete and…

存在聚集现象的模型中无需工具变量的外生性检验AI 解读

This paper presents a test of the exogeneity of a single explanatory variable in a multivariate model. It does not require the exogeneity of the other regressors or the existence of instrumental variables. The fundamental maintained assumption is that the model must be continuous in the explanatory variable of interest. This test has power when unobservable confounders are discontinuous with respect to the explanatory variable of interest, and it is particularly suitable for…

纳入基线协变量的半参数自然直接效应模型估计AI 解读

Establishing cause-effect relationships is a standard goal of empirical science. Once the presence of a causal relationship is established, the precise causal mechanism involved becomes a topic of interest. A particularly popular type of mechanism analysis concerns questions of mediation, that is to what extent an effect is direct, and to what extent it is mediated by a third variable. A semiparametric theory has recently been proposed which allows multiply robust estimation…

Q-learning 的交互式模型构建AI 解读

Evidence-based rules for optimal treatment allocation are key components in the quest for efficient, effective health care delivery. Q-learning, an approximate dynamic programming algorithm, is a popular method for estimating optimal sequential decision rules from data. Q-learning requires the modeling of nonsmooth, nonmonotone transformations of the data, complicating the search for adequately expressive, yet parsimonious, statistical models. The default Q-learning working…

风险差的广义 Cornfield 条件AI 解读

A central question in causal inference with observational studies is the sensitivity of conclusions to unmeasured confounding. The classical Cornfield condition allows us to assess whether an unmeasured binary confounder can explain away the observed relative risk of the exposure on the outcome. It states that for an unmeasured confounder to explain away an observed relative risk, the association between the unmeasured confounder and the exposure and the association between…

CAM：因果加性模型、高维排序搜索与惩罚回归AI 解读

We develop estimation for potentially high-dimensional additive structural equation models. A key component of our approach is to decouple order search among the variables from feature or edge selection in a directed acyclic graph encoding the causal structure. We show that the former can be done with nonregularized (restricted) maximum likelihood estimation while the latter can be efficiently addressed using sparse regression techniques. Thus, we substantially simplify the…