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Credible causal inference in accounting and finance research often comes from natural experiments. These experiments can be exploited using several shock-based research designs, including difference in differences (DID), shock-based instrumental variable (shock-IV), and regression discontinuity. We study here shock-IV designs using panel data. We identify all shock-IV papers in two broad data sets and reexamine three of the apparently strongest papers—Desai and Dharmapala [Desai M, Dharmapala D (2009) Corporate tax avoidance and firm value. Rev. Econom. Statist. 91:537–546.], Duchin et al. [Duchin R, Matsusaka J, Ozbas O (2010) When are outside directors effective? J. Financial Econom. 95:195–214.], and Iliev [Iliev P (2010) The effect of SOX Section 404: Costs, earnings quality, and stock prices. J. Finance 65:1163–1196.]. After we enforce covariate balance and common support for treated and control firms, the instruments in all three papers are unusable—they are no longer significant in the first stage. All three papers also show nonparallel pretreatment trends on outcomes or core covariates. The problems with these papers generalize to our full sample and to other papers exploiting the same shocks as Duchin et al. A core conclusion of our reexamination is that pretreatment balance (common support, covariate balance, and parallel pretreatment trends) is necessary for credible shock-IV designs. We provide a good-practice checklist for shock-IV design with panel data, much of which also applies to DID designs. This paper was accepted by Shiva Rajgopal, accounting.

In many causal inference problems the parameter of interest is the regression causal effect, defined as the conditional mean difference in the potential outcomes given covariates. In this paper we discuss how sufficient dimension reduction can be used to aid causal inference, and we propose a new estimator of the regression causal effect inspired by minimum average variance estimation. The estimator requires a weaker common support condition than propensity score-based approaches, and can be used to estimate the average causal effect, for which it is shown to be asymptotically super-efficient. Its finite-sample properties are illustrated by simulation.

Motivated by an observational study of the effect of hospital ward versus intensive care unit admission on severe sepsis mortality, we develop methods to address two common problems in observational studies: (1) when there is a lack of covariate overlap between the treated and control groups, how to define an interpretable study population wherein inference can be conducted without extrapolating with respect to important variables; and (2) how to use randomization inference to form confidence intervals for the average treatment effect with binary outcomes. Our solution to problem (1) incorporates existing suggestions in the literature while yielding a study population that is easily understood in terms of the covariates themselves, and can be solved using an efficient branch-and-bound algorithm. We address problem (2) by solving a linear integer program to use the worst-case variance of the average treatment effect among values for unobserved potential outcomes that are compatible with the null hypothesis. Our analysis finds no evidence for a difference between the 60-day mortality rates if all individuals were admitted to the ICU and if all patients were admitted to the hospital ward among less severely ill patients and among patients with cryptic septic shock. We implement our methodology in R, providing scripts in the supplementary material.

Causal inference in observational studies typically requires making comparisons between groups that are dissimilar. For instance, researchers investigating the role of a prolonged duration of breastfeeding on child outcomes may be forced to make comparisons between women with substantially different characteristics on average. In the extreme there may exist neighborhoods of the covariate space where there are not sufficient numbers of both groups of women (those who breastfed for prolonged periods and those who did not) to make inferences about those women. This is referred to as lack of common support. Problems can arise when we try to estimate causal effects for units that lack common support, thus we may want to avoid inference for such units. If ignorability is satisfied with respect to a set of potential confounders, then identifying whether, or for which units, the common support assumption holds is an empirical question. However, in the high-dimensional covariate space often required to satisfy ignorability such identification may not be trivial. Existing methods used to address this problem often require reliance on parametric assumptions and most, if not all, ignore the information embedded in the response variable. We distinguish between the concepts of \"common support\" and \"common causal support.\" We propose a new approach for identifying common causal support that addresses some of the shortcomings of existing methods. We motivate and illustrate the approach using data from the National Longitudinal Survey of Youth to estimate the effect of breastfeeding at least nine months on reading and math achievement scores at age five or six. We also evaluate the comparative performance of this method in hypothetical examples and simulations where the true treatment effect is known.

In massive MIMO-OFDM systems, channel estimation is a significant module which can be utilized to eliminate multipath interference. However, in realistic communication systems, carrier frequency offset (CFO), which often exists in receive end, will deteriorate the performance of channel estimation. One of the effective solutions is to compensate CFO via the help of pseudo-noise (PN) sequence. At the beginning of this paper, to reduce system complexity and correctly compensate CFO, we propose an improved OFDM frame structure. Subsequently, we theoretically analyze the catastrophic influence of CFO on conventional PN-sequence-based compressed sensing (CS) channel estimation scheme. As our solution, based on the improved OFDM frame structure, a novel massive MIMO-OFDM channel estimation method under CFO environment is proposed. It first estimates CFO by utilizing differential correlation algorithm. Thereby, the interference caused by CFO can be eliminated. Then, relying on the PN sequence, the partial common support (PCS) information of each channel is obtained. Finally, using the PCS information as a priori information, we improve the CS reconstruction scheme to estimate the accurate channel. The simulation result shows that the proposed scheme demonstrates better MSE and BER performance than other mentioned schemes. The major advantage of our scheme is its anti-CFO ability and independence to channel sparsity level. Therefore, the proposed scheme is meaningful for practical use.

The inverse probability of treatment weighted (IPTW) estimator can be used to make causal inferences under two assumptions: (1) no unobserved confounders (ignorability) and (2) positive probability of treatment and of control at every level of the confounders (positivity), but is vulnerable to bias if by chance, the proportion of the sample assigned to treatment, or proportion of control, is zero at certain levels of the confounders. We propose to deal with this sampling zero problem, also known as practical violation of the positivity assumption, in a setting where the observed confounder is cluster identity, i.e., treatment assignment is ignorable within clusters. Specifically, based on a random coefficient model assumed for the potential outcome, we augment the IPTW estimating function with the estimated potential outcomes of treatment (or of control) for clusters that have no observation of treatment (or control). If the cluster-specific potential outcomes are estimated correctly, the augmented estimating function can be shown to converge in expectation to zero and therefore yield consistent causal estimates. The proposed method can be implemented in the existing software, and it performs well in simulated data as well as with real-world data from a teacher preparation evaluation study.

Purpose: The objective of the study is to quantify the wage gap between native and immigrant women in Spain, taking into account differences in their characteristics and the need to control for common support. If immigrant women are segregated in occupations with few native women, it is important to take this into account to analyse wage differentials between both collectives. Methodology: We use microdata from the Continuous Sample of Working Histories (Muestra Continua de Vidas Laborales) on wages and other personal characteristics such as gender, country of origin, and age to apply the matching procedure and the decomposition of the wage gap along the lines of Ñopo (2008) for the analysis of wage differentials between native and immigrant women. The advantage of this procedure is that we can simultaneously estimate the common support and the mean counterfactual wage for the women on the common support (i.e., comparing native and immigrant women with similar observable characteristics). In addition, we can describe differences not only at the mean but also along the entire wage distribution. Findings: The results obtained indicate that, on average, immigrant women earn less than native women in the Spanish labour market. This wage gap is bigger when we consider immigrant women from developing countries, but our main finding is that an important part of this wage gap is related to differences in common support (i.e., immigrant women are segregated in certain jobs with low wages different from those occupied by native women). If the need to control for common support is neglected, estimates of the wage gap will be biased. Originality: Studying the case of Spain is particularly interesting because it is a country with abundant and recent immigration. Immigrant women account for more than half of the total immigrants in Spain, and unlike other host countries, they come from a highly varied range of countries, with origins as diverse as Latin America, the Maghreb and Eastern Europe. To our knowledge, no other study has explicitly focused on the analysis of the wage differential of immigrant women in the Spanish labour market by taking into account the need to control for common support. Moreover, published papers illustrating the potentiality of Ñopo\"s (2008) methodology are also very scarce.

The authors focus on the minimum sampling rate and the exact recovery condition in the sampling of multiple signals with finite rate of innovation (FRI) and sparse common support (SCS). The authors first propose the subspace-based recovery method and analyse its relation with the annihilating filter; then the proposed method is used for sampling the multiple signals with FRI and SCS. It is observed that the minimum sampling rate for the exact recovery heavily depends on the signal structure described by the defined characteristic matrix, based on which a sufficient and necessary condition is also presented. The numerical simulations show that the proposed recovery method and the recovery condition are feasible for the sampling of multiple signals with FRI and SCS.

Purpose There is a limited number of therapies with a high level of recommendations for mucositis, while several strategies are currently employed with a limited evidence for efficacy. A national survey among Italian oncologists who treat head and neck cancer (HNC) was conducted in order to assess the most common preventive and therapeutic protocols (including nutritional support and pain control) for oral mucositis (OM) in patients undergoing chemoradiotherapy. Methods From September to November 2012, a nationwide electronic survey with 21 focused items was proposed to chemotherapy and radiotherapy centers. Results We collected 111 answers. Common Terminology Criteria for Adverse Events (CTCAE) scale is employed by 55 % of the physicians in assessing mucosal toxicity. The most relevant predictive factors for OM development are considered smoke, alcohol use, planned radiotherapy, and concurrent use of radiosensitizing chemotherapy. Prophylactic gastrostomy is adopted in <10 % of the patients. Preventive antibiotics or antimycotics are prescribed by 46 % of the responders (mainly local or systemic antimycotic drugs). Alkalinizing mouthwashes or coating agents are frequently adopted (70 % of the cases). Among therapeutic interventions, systemic fluconazole is administered by 80 % of the physicians. Pain is mainly treated by weak followed by strong opioids. Conclusions A variety of preventive and therapeutic protocols for OM exists among the participating Italian centers, with some uniformity in respect to nutritional support, use of antimycotic and painkillers. There is an urgent need for well-conducted clinical trials aimed at assessing the best choices for OM prevention and treatment in HNC.

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