Semiparametric Causal Sufficient Dimension Reduction Of High Dimensional Treatments

Cause-effect relationships are typically evaluated by comparing the outcome responses to binary treatment values, representing two arms of a hypothetical randomized controlled trial. However, in certain applications, treatments of interest are continuous and high dimensional. For example, understanding the causal relationship between severity of radiation therapy, represented by a high dimensional vector of radiation exposure values and post-treatment side effects is a problem of clinical interest in radiation oncology. An appropriate strategy for making interpretable causal conclusions is to reduce the dimension of treatment. If individual elements of a high dimensional treatment vector weakly affect the outcome, but the overall relationship between the treatment variable and the outcome is strong, careless approaches to dimension reduction may not preserve this relationship. Moreover, methods developed for regression problems do not transfer in a straightforward way to causal inference due to confounding complications between the treatment and outcome. In this paper, we use semiparametric inference theory for structural models to give a general approach to causal sufficient dimension reduction of a high dimensional treatment such that the cause-effect relationship between the treatment and outcome is preserved. We illustrate the utility of our proposal through simulations and a real data application in radiation oncology

Causal Inference Methods For Bias Correction In Data Analyses

Many problems in the empirical sciences and rational decision making require causal, rather than associative, reasoning. The field of causal inference is concerned with establishing and quantifying cause-effect relationships to inform interventions, even in the absence of direct experimentation or randomization. With the proliferation of massive datasets, it is crucial that we develop principled approaches to drawing actionable conclusions from imperfect information. Inferring valid causal conclusions is impeded by the fact that data are unstructured and filled with different sources of bias. The types of bias that we consider in this thesis include: confounding bias induced by common causes of observed exposures and outcomes, bias in estimation induced by high dimensional data and curse of dimensionality, discriminatory bias encoded in data that reflect historical patterns of discrimination and inequality, and missing data bias where instantiations of variables are systematically missing. The focus of this thesis is on the development of novel causal and statistical methodologies to better understand and resolve these pressing challenges. We draw on methodological insights from both machine learning/artificial intelligence and statistical theory. Specifically, we use ideas from graphical modeling to encode our assumptions about the underlying data generating mechanisms in a clear and succinct manner. Further, we use ideas from nonparametric and semiparametric theories to enable the use of flexible machine learning modes in the estimation of causal effects that are identified as functions of observed data. There are four main contributions to this thesis. First, we bridge the gap between identification and semiparametric estimation of causal effects that are identified in causal graphical models with unmeasured confounders. Second, we use semiparametric inference theory for marginal structural models to give the first general approach to causal sufficient dimension reduction of a high dimensional treatment. Third, we address conceptual, methodological, and practical gaps in assessing and overcoming disparities in automated decision making using causal inference and constrained optimization. Fourth, we use graphical representations of missing data mechanisms and provide a complete characterization of identification of the underlying joint distribution where some variables are systematically missing and others are unmeasured

Long-term Results after Restoring Flexor Tendon Injury in Children Younger than Age 10 Years

Background: In regard to the rarity of pediatric tendon lacerations compared with the adult population, sparse knowledge exists. Published reports indicate that the incidence of “good” flexor tendon repair outcomes is low. This study aimed to determine the injury pattern and demographics of pediatric flexor tendon injuries over the past decade.Methods: A retrospective chart review of all flexor tendon injuries between 2005 and 2015 was performed. Parameters reviewed included demographics, injury mechanism, repair technique, outcomes, and complications.Results: A total of 20 patients with a median age of 4 years and 4 months experienced 45 tendon injuries. The most common cause of injury was glass (n = 10), with the most common digit injured being the index finger (n = 8). Zone II had the highest number of injuries (n = 14). The modified Kessler core and peripheral running sutures technique were used in all primary repairs (n = 18). Using author designed evaluation system, 80% of patients experienced excellent recovery. Four patients had good results. Only one patient complicated with rupture necessitating further surgery that its final evaluation was excellent.Conclusions: The outcome of restoring flexor tendon injury of children is satisfactory, and we recommend that

Misnomers in Hand Surgery

Hand surgery literature is full of disease names and terms. Some of them are misnomers, which are misleading to physicians outside the specialty. Therefore, we decided to collect all misnomers and provide them via this paper. Considering development of sciences in future, perhaps avoidance from new misnomers is impossible, but awareness of this fact, lead us to be more ingenious in interpretation. On the other hand, we believe his collection would be interesting for most specialists in hand surgery and as well informative for others

On Discrimination Discovery and Removal in Ranked Data using Causal Graph

Predictive models learned from historical data are widely used to help companies and organizations make decisions. However, they may digitally unfairly treat unwanted groups, raising concerns about fairness and discrimination. In this paper, we study the fairness-aware ranking problem which aims to discover discrimination in ranked datasets and reconstruct the fair ranking. Existing methods in fairness-aware ranking are mainly based on statistical parity that cannot measure the true discriminatory effect since discrimination is causal. On the other hand, existing methods in causal-based anti-discrimination learning focus on classification problems and cannot be directly applied to handle the ranked data. To address these limitations, we propose to map the rank position to a continuous score variable that represents the qualification of the candidates. Then, we build a causal graph that consists of both the discrete profile attributes and the continuous score. The path-specific effect technique is extended to the mixed-variable causal graph to identify both direct and indirect discrimination. The relationship between the path-specific effects for the ranked data and those for the binary decision is theoretically analyzed. Finally, algorithms for discovering and removing discrimination from a ranked dataset are developed. Experiments using the real dataset show the effectiveness of our approaches.Comment: 9 page

Semiparametric Sensitivity Analysis: Unmeasured Confounding In Observational Studies

Establishing cause-effect relationships from observational data often relies on untestable assumptions. It is crucial to know whether, and to what extent, the conclusions drawn from non-experimental studies are robust to potential unmeasured confounding. In this paper, we focus on the average causal effect (ACE) as our target of inference. We generalize the sensitivity analysis approach developed by Robins et al. (2000), Franks et al. (2020) and Zhou and Yao (2023. We use semiparametric theory to derive the non-parametric efficient influence function of the ACE, for fixed sensitivity parameters. We use this influence function to construct a one-step bias-corrected estimator of the ACE. Our estimator depends on semiparametric models for the distribution of the observed data; importantly, these models do not impose any restrictions on the values of sensitivity analysis parameters. We establish sufficient conditions ensuring that our estimator has root-n asymptotics. We use our methodology to evaluate the causal effect of smoking during pregnancy on birth weight. We also evaluate the performance of estimation procedure in a simulation study