Matrix Completion When Missing Is Not at Random and Its Applications in Causal Panel Data Models

This paper develops an inferential framework for matrix completion when missing is not at random and without the requirement of strong signals. Our development is based on the observation that if the number of missing entries is small enough compared to the panel size, then they can be estimated well even when missing is not at random. Taking advantage of this fact, we divide the missing entries into smaller groups and estimate each group via nuclear norm regularization. In addition, we show that with appropriate debiasing, our proposed estimate is asymptotically normal even for fairly weak signals. Our work is motivated by recent research on the Tick Size Pilot Program, an experiment conducted by the Security and Exchange Commission (SEC) to evaluate the impact of widening the tick size on the market quality of stocks from 2016 to 2018. While previous studies were based on traditional regression or difference-in-difference methods by assuming that the treatment effect is invariant with respect to time and unit, our analyses suggest significant heterogeneity across units and intriguing dynamics over time during the pilot program

Inference for Low-rank Models without Estimating the Rank

This paper studies the inference about linear functionals of high-dimensional low-rank matrices. While most existing inference methods would require consistent estimation of the true rank, our procedure is robust to rank misspecification, making it a promising approach in applications where rank estimation can be unreliable. We estimate the low-rank spaces using pre-specified weighting matrices, known as diversified projections. A novel statistical insight is that, unlike the usual statistical wisdom that overfitting mainly introduces additional variances, the over-estimated low-rank space also gives rise to a non-negligible bias due to an implicit ridge-type regularization. We develop a new inference procedure and show that the central limit theorem holds as long as the pre-specified rank is no smaller than the true rank. Empirically, we apply our method to the U.S. federal grants allocation data and test the existence of pork-barrel politics

Inference for Low-rank Completion without Sample Splitting with Application to Treatment Effect Estimation

This paper studies the inferential theory for estimating low-rank matrices. It also provides an inference method for the average treatment effect as an application. We show that the least square estimation of eigenvectors following the nuclear norm penalization attains the asymptotic normality. The key contribution of our method is that it does not require sample splitting. In addition, this paper allows dependent observation patterns and heterogeneous observation probabilities. Empirically, we apply the proposed procedure to estimating the impact of the presidential vote on allocating the U.S. federal budget to the states

Discovery of Q203, a potent clinical candidate for the treatment of tuberculosis

New therapeutic strategies are needed to combat the tuberculosis pandemic and the spread of multidrug-resistant (MDR) and extensively drug-resistant (XDR) forms of the disease, which remain a serious public health challenge worldwide1, 2. The most urgent clinical need is to discover potent agents capable of reducing the duration of MDR and XDR tuberculosis therapy with a success rate comparable to that of current therapies for drug-susceptible tuberculosis. The last decade has seen the discovery of new agent classes for the management of tuberculosis3, 4, 5, several of which are currently in clinical trials6, 7, 8. However, given the high attrition rate of drug candidates during clinical development and the emergence of drug resistance, the discovery of additional clinical candidates is clearly needed. Here, we report on a promising class of imidazopyridine amide (IPA) compounds that block Mycobacterium tuberculosis growth by targeting the respiratory cytochrome bc1 complex. The optimized IPA compound Q203 inhibited the growth of MDR and XDR M. tuberculosis clinical isolates in culture broth medium in the low nanomolar range and was efficacious in a mouse model of tuberculosis at a dose less than 1 mg per kg body weight, which highlights the potency of this compound. In addition, Q203 displays pharmacokinetic and safety profiles compatible with once-daily dosing. Together, our data indicate that Q203 is a promising new clinical candidate for the treatment of tuberculosis

Lead Optimization of a Novel Series of Imidazo[1,2‑<i>a</i>]pyridine Amides Leading to a Clinical Candidate (Q203) as a Multi- and Extensively-Drug-Resistant Anti-tuberculosis Agent

A critical unmet clinical need to combat the global tuberculosis epidemic is the development of potent agents capable of reducing the time of multi-drug-resistant (MDR) and extensively-drug-resistant (XDR) tuberculosis therapy. In this paper, we report on the optimization of imidazo­[1,2-<i>a</i>]­pyridine amide (IPA) lead compound <b>1</b>, which led to the design and synthesis of Q203 (<b>50</b>). We found that the amide linker with IPA core is very important for activity against Mycobacterium tuberculosis H37Rv. Linearity and lipophilicity of the amine part in the IPA series play a critical role in improving in vitro and in vivo efficacy and pharmacokinetic profile. The optimized IPAs <b>49</b> and <b>50</b> showed not only excellent oral bioavailability (80.2% and 90.7%, respectively) with high exposure of the area under curve (AUC) but also displayed significant colony-forming unit (CFU) reduction (1.52 and 3.13 log₁₀ reduction at 10 mg/kg dosing level, respectively) in mouse lung

Discovery of Q203, a potent clinical candidate for the treatment of tuberculosis.

New prophylactic and therapeutic strategies are needed to combat the tuberculosis pandemic and the spread of extensively-drug resistant form of the disease. During the course of a high-content chemical screen, ImidazoPyridine Amides (IPA) were identified as a promising class of anti-tubercular agents. The optimized IPA compound Q203 inhibits the growth of multi- and extensively-drug resistant clinical isolates of M. tuberculosis in the low nanomolar range. Q203 was efficacious in vivo at a dose below 1mg/kg, making this compound one of the most potent discovered up to date. In addition, it shows pharmacokinetic and safety profiles compatible with once daily dosing. A reverse genetic approach identifies the ubiquinol cytochrome C reductase (QcrB, Rv2196) as the target of Q203. Mode of action studies revealed that Q203 inhibits the process of ATP synthesis in both replicating and hypoxic non-replicating M. tuberculosis. Altogether, our data indicates that Q203 is a promising clinical candidate for the treatment of tuberculosis