Claims from the Trump White House about links between use of the painkiller acetaminophen – often sold under the brand name Tylenol in the U.S. – during pregnancy and development of autism have set off a deluge of responses across the medical, scientific and public health communities.

As a father of a child with level 2 autism – meaning autism that requires substantial support – and a statistician who works with such tools as those used in the association studies cited by the White House, I find it useful to think about the nuances of association versus causation in observational studies. I hope that this explanation is helpful to parents and expecting parents who, like me, are deeply invested in the well-being of their children.

Association is not causation, but …

Most people have heard this before, but it bears repeating: Association does not imply causation.

An often-cited example is that there is a very strong association between ice cream sales and incidents of shark attacks. Of course, it goes without saying that shark attacks aren’t caused by ice cream sales. Rather, in the summertime, hot weather drives more appetite for ice cream and beach time. The increased number of people at the beach does, in turn, cause the likelihood of shark attacks to increase.

Yet pointing this out on its own is neither intellectually satisfying nor emotionally appeasing when it comes to real-life medical concerns, since an association does suggest potential for a causal relationship.

In other words, some associations do end up being convincingly causal. In fact, some of the most consequential discoveries of the past century in public health, like the links between smoking and lung cancer or the human papillomavirus (HPV) and cervical cancer, started out as findings of very strong association.

So when it comes to the issue of prenatal acetaminophen use and autism development, it is important to consider how strong the association found is, as well as the extent to which such an association could be considered causal.

Establishing causal association

So how do scientists determine if an observed association is actually causal?

The gold standard for doing so is conducting what are called randomized, controlled experiments. In these studies, participants are randomly assigned to receive treatment or not, and the environment where they are observed is controlled so that the only external element that differs among participants is whether they received treatment or not.

In doing this, researchers reasonably ensure that any difference in the outcomes of the participants can be directly attributed as being caused by whether they received the treatment. That is, any association between treatment and outcome can be considered causal.

Yet oftentimes, conducting such an experiment is impossible, unethical or both. For instance, it would be highly difficult to gather a cohort of pregnant women for an experiment and extremely unethical to randomly assign half of them to take acetaminophen, or any other medication for no particular reason, and the other half not to.

So when experiments are simply infeasible, an alternative is to make some reasonable assumptions on how observational data would behave if the association was causal and then see if the data aligns with these causal assumptions. This can very broadly be referred to as observational causal inference.

Parsing what the studies mean

So how does this apply to the current controversy over the potential for acetaminophen use during pregnancy to affect the fetus in a way that could result in a condition like autism?

Researchers who try to understand causal roles and links between one variable and potential health outcomes do so by considering: 1) the size and consistency of the association across multiple attempts to estimate it, and 2) the extent to which such association has been established under observational causal inference frameworks.

As early as 1987, researchers have been working to measure possible associations between acetaminophen use during pregnancy and autism. A number of these studies, including multiple large systematic reviews, have found evidence of such associations.

For instance, a 2025 review of 46 studies that examined association between acetaminophen use and an array of neurodevelopmental disorders, including autism, identified papers with five positive associations between acetaminophen and autism.

In one of those studies, which examined 73,881 births, the researchers found that children who were exposed to acetaminophen prenatally were 20% more likely to develop borderline or clinical autism spectrum conditions. Another examined 2.48 million births and reported an estimated association of only 5%.

Both of those are weak associations. For context, estimations of increased lung cancer risk from smoking in the 1950s were between 900% to 1,900%. That is, a smoker is 10 to 20 times more likely than a nonsmoker to develop lung cancer. By comparison, in the two autism studies above, a pregnant woman who takes acetaminophen is 1.05 to 1.20 times more likely than one who does not take the drug to have a child who would be later diagnosed with autism.

It’s also important to keep in mind that many factors can affect how well a study is able to estimate an association. In general, larger sample sizes provide both greater power to detect an association if one does exist, as well as improved precision over estimating the value of the association. This does not mean that studies with smaller sample sizes are not valid, only that from a statistical perspective, researchers like me place greater confidence in an association drawn from a larger sample size.

Once an association – even a small one – is established, researchers then must consider the extent to which causation can be claimed. One way to do this is through what’s called dose-response. This means looking at whether the association is higher among women who took higher doses of acetaminophen during pregnancy.

The study mentioned above that looked at 2.48 million births shows an example of dose-response. It found that pregnant women who reported taking higher doses have higher autism risk.

Another way to examine possible causality in this context is to analyze sibling outcomes, which that same paper did. Researchers looked at whether associations between acetaminophen and autism persisted within families with more than one child.

For example, in a family with two children, if the mother used acetaminophen during one pregnancy and that child was later diagnosed with autism, but she did not use it during the other pregnancy and that child was not diagnosed, then this strengthens the causal claim. Conversely, if acetaminophen was used during the pregnancy of the child who was not diagnosed with autism and not used during the pregnancy of the child who was, then that weakens the causal claim. When this was included in the analysis, the dose-response disappeared, and in fact the overall 5% increased risk mentioned before likewise disappeared. This weakens the claim of a causal relationship.

Consult your doctor

At present, there is clearly not enough evidence to establish a causal association between prenatal acetaminophen use and autism.

Yet as a parent who wonders if my daughter will ever be able to write her name, or hold a job or raise kids of her own, I understand that such explanations may not appease the fears or concerns of an expecting mother who is suffering from a fever.

Naturally, all of us want absolute certainty.

But that’s not possible when it comes to acetaminophen use, at least not at this time.

Your doctor will be able to provide you with much sounder advice than any existing study on this topic. Your OB-GYNs are very likely aware of these studies and have much better judgment as to how these results should be considered in the context of your personal medical history and needs.

Researchers, meanwhile, will continue to dig deeper into the science of this critically important issue and, hopefully, provide greater clarity in the years to come.

Mark Louie Ramos, Assistant Research Professor of Health Policy and Administration, Penn State

This article is republished from The Conversation under a Creative Commons license. Read the original article.

As the federal fiscal year draws to a close, an increasingly familiar prospect is drawing near in Washington, D.C.: a possible government shutdown. And for federal workers, it couldn’t come at a worse time.

In the fractious and polarized political landscape of the United States, Democrats and Republicans have come to rely on short-term, stopgap funding bills to keep the government operating in the absence of elusive longer-term budget deals.

With the parties currently wide apart over the terms of even a short-term budget resolution, the government is set to shut down on Oct. 1, 2025, barring an 11th-hour deal that appears far off. If the shutdown does happen, it would mark another difficult moment this year for a federal workforce that has so far shed more than 300,000 jobs. This is largely due to ongoing Trump administration efforts to downsize parts of the federal government and restructure or largely eliminate certain government agencies with the stated aim of increasing efficiency.

With a government shutdown, hundreds of thousands of federal employees would be furloughed – sent home without pay until funding resumes.

As a team of financial economists who study labor markets and public sector employment and have examined millions of federal personnel records spanning such government shutdowns in the past, we have found that the consequences reach far beyond the now-familiar images of closed national parks and stalled federal services. Indeed, based on our study of an October 2013 shutdown during which about 800,000 federal employees were furloughed for 16 days, shutdowns leave an enduring negative effect on the federal workforce, reshaping its composition and weakening its performance for years to come.

What happens to workers

Millions of Americans interact with the federal government every day in ways both big and small. More than one-third of U.S. national spending is routed through government programs, including Medicare and Social Security. Federal workers manage national parks, draft environmental regulations and help keep air travel safe.

Whatever one’s political leanings, if the goal is a government that handles these responsibilities effectively, then attracting and retaining a talented workforce is essential.

Yet the ability of the federal government to do so may be increasingly difficult, in part because prolonged shutdowns can have hidden effects.

When Congress fails to pass appropriations, federal agencies must furlough employees whose jobs are not deemed “excepted” – sometimes commonly referred to as essential. Those excepted employees keep working, while others are barred from working or even volunteering until funding resumes. Furlough status reflects funding sources and mission categories, not an individual’s performance, so it confers no signal about an employee’s future prospects and primarily acts as a shock to morale.

Importantly, furloughs do not create long-term wealth losses; back pay has always been granted and, since 2019, is legally guaranteed. Employees therefore recover their pay even though they may face real financial strain in the short run.

A cynical observer might call furloughs a paid vacation, yet the data tells a different story.

Immediate consequences, longer-term effects

Using extensive administrative records on federal civilian workers from the October 2013 shutdown, we tracked how this shock to morale rippled through government operations. Employees exposed to furloughs were 31% more likely to leave their jobs within one year.

These departures were not quickly replaced, forcing agencies to rely on costly temporary workers and leading to measurable declines in core functions such as payment accuracy, legal enforcement and patenting activity.

Further, we found that this exodus builds over the first two years after the shutdown and then settles into a permanently lower headcount, implying a durable loss of human capital. The shock to morale is more pronounced among young, female and highly educated professionals with plenty of outside options. Indeed, our analysis of survey data from a later 2018-2019 shutdown confirms that morale, not income loss, drives the exits.

Employees who felt most affected reported a sharp drop in agency, control and recognition, and they were far more likely to plan a departure.

The effect of the motivation loss is striking. Using a simple economic model where workers can be expected to value both cash and purpose, we estimate that the drop in intrinsic motivation after a shutdown would require a roughly 10% wage raise to offset.

Policy implications

Some people have argued that this outflow of employees amounts to a necessary trimming, a way to shrink government by a so-called starving of the beast.

But the evidence paints a different picture. Agencies hit hardest by furloughs turned to temporary staffing firms to fill the gaps. Over the two years after the shutdown we analyzed, these agencies spent about US$1 billion more on contractors than they saved in payroll.

The costs go beyond replacement spending, as government performance also suffers. Agencies that were more affected by the shutdown recorded higher rates of inaccurate federal payments for several years. Even after partial recovery, losses amounted to hundreds of millions of dollars that taxpayers never recouped.

Other skill-intensive functions declined as well. Legal enforcement fell in agencies that became short of experienced attorneys, and patenting activity dropped in science and engineering agencies after key inventors left.

Official estimates of shutdown costs typically focus on near-term GDP effects and back pay. But our findings show that an even bigger bill comes later in the form of higher employee turnover, higher labor costs to fill gaps, and measurable losses in productivity.

Shutdowns are blunt, recurring shocks that demoralize the public workforce and erode performance. These costs spill over to everyone who relies on government services. If the public wants efficient, accountable public institutions, then we should all care about avoiding shutdowns.

After an already turbulent year, it is unclear whether an upcoming shutdown would significantly add to the strain on federal employees or have a more limited effect, since many who were considering leaving have already left through buyouts or forced terminations this year. What is clear is that hundreds of thousands of federal employees are likely to experience another period of uncertainty.

Gonzalo Maturana, Associate Professor of Finance, Emory University; Andrew Teodorescu, Ph.D. Candidate in Finance, Stanford University, and Christoph Herpfer, Assistant Professor of Business Administration, University of Virginia

This article is republished from The Conversation under a Creative Commons license. Read the original article.