Title: We will announce soon
Abstract: We will announce soon.

Ms. Yiyi Huo
UW-Seattle, USA

Title：On the adaptation of causal forests to manifold data
Abstract: Researchers often hold the belief that random forests are "the cure to the world's ills". But how exactly do they achieve this? Focused on the recently introduced causal forests, we aim to contribute to an ongoing research trend towards answering this question, proving that causal forests can adapt to the unknown covariate manifold structure. In particular, our analysis shows that a causal forest estimator can achieve the optimal rate of convergence for estimating the conditional average treatment effect, with the covariate dimension automatically replaced by the manifold dimension.

Prof. Zhichao Jiang
Sun Yat-sen University, China

Title: Principal Stratification with Continuous Post-Treatment Variables
Abstract: Post-treatment variables often complicate causal inference. They appear in many scientific problems, including noncompliance, truncation by death, mediation, and surrogate endpoint evaluation. Principal stratification is a strategy that adjusts for the potential values of the post-treatment variables, defined as the principal strata. It allows for characterizing treatment effect heterogeneity across principal strata and unveiling the mechanism of the treatment on the outcome related to post-treatment variables. However, the existing literature has primarily focused on binary post-treatment variables, leaving the case with continuous post-treatment variables largely unexplored, due to the complexity of infinitely many principal strata that challenge both the identification and estimation of causal effects. We fill this gap by providing nonparametric identification and semiparametric estimation theory for principal stratification with continuous post-treatment variables. We propose to use working models to approximate the underlying causal effect surfaces and derive the efficient influence functions of the corresponding model parameters.

Prof. Theis Lange
University of Copenhagen, Denmark

Title: We will announce soon
Abstract: We will announce soon

Prof. Mark Van Der Lann
University of California, Berkeley, USA

Title: Targeted Learning, HAL, and Causal Inference for Generating Real World Evidence in Drug Development
Abstract: Targeted Learning follows a general roadmap for 1) accurately translating the real world into a formal statistical estimation problem in terms  of causal estimand, a corresponding statistical estimand, and statistical model; 2) a corresponding template for construction of a targeted maximum likelihood estimator (TMLE) of the statistical estimand; and finally 3) a sensitivity analysis addressing the possible causal gap. The TMLE represents an optimal plug-in machine learning based estimator of the estimand combined with formal statistical inference. The three pillars of TMLE are super-learning, Highly Adaptive Lasso (HAL), and the TMLE-update step, where the latter has various choices such as CV-TMLE/C-TMLE, and the recently developed adaptive TMLE (Lars van der Laan et al., 2023).  Through super-learning it can  incorporate high dimensional and diverse data sources such as images, NLP features, and state of art algorithms tailored for such data sources. To optimize finite sample performance, the precise specification of TMLE can be tailored towards the precise experiment and statistical estimation problem in question, while being theoretically grounded, optimal, and benchmarked.  We provide a motivation, explanation, and overview of targeted learning; the key role of super-learning and HAL; discuss some of the key choices and considerations in specifying the TMLE-step; and discuss (a priori specified) SAP construction based on targeted learning, incorporating outcome-blind simulations to choose a best specification of the SAP. We also discuss a Sentinel and FDA RWE demonstration project of targeted learning demonstrating SAP specification on real data.

Assoc. Prof. Wei Li
Renming University of China, China

Title: Inference of Possibly Bi-directional Causal Relationships with Invalid Instrumental Variables
Abstract: Learning causal relationships between pairs of complex traits from observational studies is of great interest across various scientific domains. However, most existing methods assume the absence of unmeasured confounding and restrict relationships between two traits to be uni-directional, which may be violated in real-world systems. In this paper, we address the challenge of causal discovery and effect estimation for two traits while accounting for unmeasured confounding and potential feedback loops. By leveraging possibly invalid instrumental variables, we establish sufficient identifying conditions for bi-directional and uni-directional models, respectively. Then we introduce a data-driven procedure to detect whether the causal relationship is bi-directional. When it is detected to be uni-directional, we propose another procedure to determine the causal direction. We show that our method can consistently identify the true direction between two traits. Additionally, we provide estimation and inference results about causal effects along the identified direction. The proposed estimators are asymptotically normal under certain regularity conditions. We demonstrate the proposed method via simulations and real data examples from UK Biobank.

Asst. Prof. Xinran Li
University of Chicago, USA

Title: Robust Sensitivity Analysis for Matched Observational Studies
Abstract: Observational studies provide invaluable opportunities for causal inference, but they often suffer from biases due to pretreatment difference between treated and control units. Matching has been a popular approach to reduce observed covariate imbalance. To tackle the unmeasured confounding, Rosenbaum proposed a sensitivity analysis framework for matched observational studies, which adapts and extends the conventional randomization inference for randomized experiments. However, Rosenbaum’s analysis may exhibit two potential limitations. First, it focuses mainly on sharp null hypotheses, say Fisher’s null of no effect for any unit, which can be restrictive in practice and are not able to accommodate unknown individual effect heterogeneity. Second, it considers mainly a uniform bound on the strength of hidden confounding across matched sets, under which the sensitivity analysis will lose power if extreme hidden confounding is suspected, e.g., some units may be almost certain to take the treatment or control due to unmeasured confounding. In this talk, we will extend Rosenbaum’s framework to overcome both limitations. First, we propose sensitivity analysis for quantiles of individual treatment effects, without any constant-effects assumptions. Second, we will propose sensitivity analysis based on quantiles of hidden biases, which can strengthen the evidence supporting a causal finding.

Asst. Prof. Zhaotong Lin
Florida State University, USA

Title: A Robust Cis-Mendelian Randomization Method with Application to Drug Target Discovery
Abstract: Mendelian randomization (MR) uses genetic variants as instrumental variables (IVs) to investigate a causal relationship between two traits, an exposure and an outcome. Compared to conventional MR using only independent IVs selected from the whole genome, cis-MR focuses on a single genomic region using only cis-SNPs. For example, using cis-pQTLs for each circulating protein as an exposure for a disease opens an economical path for drug target discovery. Despite the significance of such applications, only few methods are robust to (horizontal) pleiotropy and linkage disequilibrium (LD) of cis-SNPs as IVs. In this work, we propose a cis-MR method based on constrained maximum likelihood, called cisMR-cML, which accounts for LD and (horizontal) pleiotropy in a general likelihood framework. It is robust to the violation of any of the three valid IV assumptions with strong theoretical support. We further clarify the severe but largely neglected consequence of the current practice of modeling marginal effects, instead of conditional effects, of SNPs in cis-MR analysis. Numerical studies demonstrated the advantage of our method over other existing methods. We applied our method in a drug-target analysis for coronary artery disease (CAD), including a proteome-wide application, in which three potential drug targets, PCSK9, COLEC11 and FGFR1, for CAD were identified.

Asst. Prof. Lin Liu
Shanghai Jiaotong University, China

Title: DNA-SE: Towards Deep Neural-Nets Assisted Semiparametric Estimation
Abstract: Semiparametric statistics play a pivotal role in a wide range of domains, including but not limited to missing data, causal inference, and transfer learning, to name a few. In many settings, semiparametric theory leads to (nearly) statistically optimal procedures that yet involve numerically solving Fredholm integral equations of the second kind. Traditional numerical methods, such as polynomial approximations or grid-based methods, are difficult to scale to multi-dimensional problems. Alternatively, statisticians may choose to approximate the original integral equations by ones with closed-form solutions, resulting in computationally more efficient, but statistically suboptimal or even incorrect procedures. To bridge this gap, we propose a new framework by formulating the semiparametric estimation problem as a bi-level optimization problem; and then we develop a scalable algorithm called Deep Neural-Nets Assisted Semiparametric Estimation (DNA-SE) by leveraging the universal approximation property of Deep Neural-Nets (DNN) to streamline semiparametric procedures. Through extensive numerical experiments and a real data analysis, we demonstrate the numerical and statistical advantages of (DNA-SE) over traditional methods.

Assoc. Prof. Ruixuan Liu
The Chinese University of Hong Kong, China

Title: Double Robust Bayesian Inference on Average Treatment Effects
Abstract: We propose a double robust Bayesian inference procedure on the average treatment effect (ATE) under unconfoundedness. Our robust Bayesian approach involves two important modifications: first, we adjust the prior distributions of the conditional mean function; second, we correct the posterior distribution of the resulting ATE. Both steps make use of a pilot estimator of the Riesz representor. We prove asymptotic equivalence of our Bayesian estimator and double robust frequentist estimators by establishing a new semiparametric Bernstein-von Mises theorem under double robustness; i.e., the lack of smoothness of conditional mean functions can be compensated by high regularity of the propensity score and vice versa. Consequently, the resulting Bayesian point estimator internalizes the bias correction and the Bayesian credible sets form confidence intervals with asymptotically exact coverage probability. In simulations, our robust Bayesian procedure leads to significant bias reduction of point estimation and accurate coverage of confidence intervals, especially when the dimensionality of covariates is large relative to the sample size and the underlying functions become complex. We illustrate our method in an application to the National Supported Work Demonstration.

Asst. Prof. Zhonghua Liu
Columbia University, USA

Title: Robust Mendelian Randomization Coupled with Alphafold2 for Drug Target Discovery
Abstract: Mendelian randomization (MR) uses genetic variants as instrumental variables (IVs) to infer the causal effect of a modifiable exposure on the outcome of interest by removing unmeasured confounding bias. However, some genetic variants might be invalid IVs due to violations of core IV assumptions. MR analysis with invalid IVs might lead to biased causal effect estimate and misleading scientific conclusions. To address this challenge, we propose a novel MR method that first Selects valid genetic IVs and then performs Post-selection Inference (MR-SPI) based on two-sample genome-wide summary statistics. We analyze 912 plasma proteins using the large-scale UK Biobank proteomics data in 54,306 participants and identify 7 proteins significantly associated with the risk of Alzheimer’s disease. We employ AlphaFold2 to predict the 3D structural alterations of these 7 proteins due to missense genetic variations, providing new insights into their biological functions in disease etiology.

Asst. Prof. Francesco Locatello
Institute of Science and Technology Austria

Title: We will annonuce soon
Abstract: We will annonuce soon.

Prof. Wenbin Lu
North Carolina State University, USA

Title: Off-Policy Evaluation with Irregularly-Spaced, Outcome-Dependent Observation Times
Abstract: While the classic off-policy evaluation (OPE) literature commonly assumes decision time points to be evenly spaced for simplicity, in many real-world scenarios, such as those involving user-initiated visits, decisions are made at irregularly-spaced and potentially outcome-dependent time points. For a more principled policy evaluation, this paper introduces a novel OPE framework which concerns not only the (state-action) decision-making process but also an observation process that dictates the time points at which decisions are made. Two distinct value functions, derived from cumulative rewards and integrated rewards respectively, are formulated within the framework. Statistical inference for each value function is developed under modified Markov and time-homogeneous assumptions. The validity of our method is further supported by theoretical results, simulation studies, and a real-world application in dental treatment.

Assoc. Prof. Kosuke Morikawa
Osaka University, Japan

Title: Singular Propensity Score: Reducing Variance in Weighted Estimators
Abstract: In the fields of survey sampling, missing data analysis, and causal inference, researchers often have access to only a subset of the population of interest, which can lead to biased results. To correct this bias, weighting the estimating equations with the inverse of the propensity scores is a common method. However, this approach encounters challenges when propensity scores are extremely close to 0 or 1, as the inverse probabilities may cause divergence, thereby increasing the variance of the estimates. To address this issue, this talk introduces a singular propensity score that incorporates upper and lower bounds. We propose an information criterion specifically designed for selecting these bounds, based on observed data, and propose a new weighted estimator that aims to minimize mean squared error.

Asst. Prof. Daniel Malinsky
Columbia University, USA

Title: Post-selection inference for causal effects after causal discovery
Abstract: Algorithms for constraint-based causal discovery select graphical causal models from among a space of possible candidates (e.g., all directed acyclic graphs) by executing a sequence of conditional independence tests. These may be used to inform the estimation of causal effects (e.g., average treatment effects) when there is uncertainty about which covariates ought to be adjusted for, or which variables act as confounders versus mediators. However, naively using the data twice, for model selection and estimation, would lead to invalid confidence intervals. Moreover, if the selected graph is incorrect, the inferential claims may apply to a chosen functional that is distinct from the actual causal effect. We propose an approach to post-selection inference that is based on a resampling procedure, that essentially performs causal discovery multiple times with randomly varying intermediate test statistics. Then, an estimate of the target causal effect and corresponding confidence sets are constructed from a union of individual graph-based estimates and intervals. We show that this construction has asymptotically correct coverage. Though most of our exposition focuses on the PC algorithm for learning directed acyclic graphs and the multivariate Gaussian case for simplicity, the approach is general and modular, so it can be used with other conditional independence based discovery algorithms and (semi-)parametric families. This is joint work with Ting-Hsuan Chang and Zijian Guo.