Estimating the counterfactual outcome of treatment is essential for decision-making in public health and clinical science, among others. Often, treatments are administered in a sequential, time-varying manner, leading to an exponentially increased number of possible counterfactual outcomes. Furthermore, in modern applications, the outcomes are high-dimensional and conventional average treatment effect estimation fails to capture disparities in individuals. To tackle these challenges, we propose a novel conditional generative framework capable of producing counterfactual samples under time-varying treatment, without the need for explicit density estimation. Our method carefully addresses the distribution mismatch between the observed and counterfactual distributions via a loss function based on inverse probability weighting. We present a thorough evaluation of our method using both synthetic and real-world data. Our results demonstrate that our method is capable of generating high-quality counterfactual samples and outperforms the state-of-the-art baselines.

This is a rush transcript of "Tucker Carlson Tonight" on February 9, 2022. This copy may not be in its final form and may be updated. It would be pretty fascinating to see the Democratic Party's latest internal polling on COVID restrictions. We haven't seen it, but it must have been pretty awful, apocalyptic, because something spooked them bad. Over the course of less than a week, the same people who have systematically turned America into a quarantine camp suddenly out of nowhere started calling in unison for medical freedom. Suddenly, they sound like Bobby Kennedy, Jr., pretty much all of them, even the whiny hypochondriacs at "The Atlantic" Magazine, those neurotic cat owners who've turned COVID hysteria into a religion are now calling for a total abandonment of all corona restrictions. Open everything, "The time to end pandemic restrictions is now." Believe it or not, that was the headline on "The Atlantic's" website today. So if even "The Atlantic" has given up on corona restrictions, obviously the pandemic is over. You should know this virus was killed not by science, but by the midterm elections. It turns out the only real cure for COVID-19 is the political ambition of the Democratic Party. Yes, every upside has a downside. It means that pasty NPR listeners are going to emerge from their apartment for the first time in two years, they will be loose on the streets. You're going to see them at Whole Foods again, shuffling along with their tote bags, looking bewildered and annoyed. That's bad, but it's still worth it, anything to make the insanity go away, we're celebrating. But we're also looking forward, and the question is, how do we guarantee that nothing like this ever happens again? How do we prevent future mass hysteria events in the United States?

It'd be pretty fascinating to see the Democratic Party's latest internal polling on COVID restrictions. We haven't seen it, but it must have been pretty awful, apocalyptic, because something spooked them bad. Over the course of less than a week, the same people who have systematically turned America into a quarantine camp suddenly, out of nowhere, started calling in unison for medical freedom. Suddenly, they sounded like Bobby Kennedy Jr., pretty much all of them. Even the whiny hypochondriacs at The Atlantic Magazine, those neurotic cat owners who've turned COVID hysteria into a religion are now calling for a total abandonment of all Coronavirus restrictions. Believe it or not, that was the headline on The Atlantic's website today.

Dr. Tom Frieden weighs in on what's next after the Omicron variant. Children have a more difficult time recognizing faces that are masked than adults, which could harm their ability to "navigate through social interactions with their peers and teachers," according to a newly released study. Erez Freud, a researcher at York University, who published his findings on Monday in the journal Cognitive Research: Principles & Implications. Freud, along with two professors from Israel's Ben-Gurion University, gave 72 children between the ages of 6 and 14 the Cambridge Face Memory Test, which measures facial perception abilities by presenting people with and without masks while upright and inverted. When masks were included in the presentation, it led to a "profound deficit in face perception abilities" that was "more pronounced in children compared to adults," according to the study.

The recent outbreak of the COVID-19 shocked humanity leading to the death of millions of people worldwide. To stave off the spread of the virus, the authorities in the US, employed different strategies including the mask mandate (MM) order issued by the states' governors. Although most of the previous studies pointed in the direction that MM can be effective in hindering the spread of viral infections, the effectiveness of MM in reducing the degree of exposure to the virus and, consequently, death rates remains indeterminate. Indeed, the extent to which the degree of exposure to COVID-19 takes part in the lethality of the virus remains unclear. In the current work, we defined a parameter called the average death ratio as the monthly average of the ratio of the number of daily deaths to the total number of daily cases. We utilized survey data provided by New York Times to quantify people's abidance to the MM order. Additionally, we implicitly addressed the extent to which people abide by the MM order that may depend on some parameters like population, income, and political inclination. Using different machine learning classification algorithms we investigated how the decrease or increase in death ratio for the counties in the US West Coast correlates with the input parameters. Our results showed a promising score as high as 0.94 with algorithms like XGBoost, Random Forest, and Naive Bayes. To verify the model, the best performing algorithms were then utilized to analyze other states (Arizona, New Jersey, New York and Texas) as test cases. The findings show an acceptable trend, further confirming usability of the chosen features for prediction of similar cases.