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By Lawrence Hurley

WASHINGTON(Reuters) - In a hotly contested case before the U.S. Supreme Court three years ago, Justice Anthony Kennedy cast the deciding vote to uphold the broad scope of race discrimination claims that can be brought under the Fair Housing Act.

While Kennedy's legal decisions were often friendly to corporate interests, his rulings - as in the Fair Housing case - sometimes went against them, especially in cases involving civil rights and environmental regulation.

Now, his impending departure from the court, announced on Wednesday, has raised hopes among some business interests that his replacement will be more consistently in their corner.

"Kennedy was not doctrinaire and was sometimes unpredictable. The outcomes of his decisions weren’t consistently pro-business or consistently anti-business. He took every case as it came to him," said John Elwood, a Washington lawyer who previously served as a law clerk to Kennedy.

Kennedy will be replaced with a justice selected by President Donald Trump, who has said he will make his choice from a shortlist of conservative judges.

One area in which that could lead to more business-friendly decisions is in "disparate impact" litigation, a concept at the core of the Fair Housing Act fight.

Under the principle, lawsuits can be won when an action has an outsized negative effect on a specific ethnic, racial, gender or other group, even if there is no evidence of discriminatory intent.

The court's jurisprudence is unlikely to change radically on some other business matters, since Kennedy was generally a reliable vote for corporate interests on issues such as curbing class action lawsuits by consumers and employees and endorsing the increasing use of arbitration agreements.

The disparate impact question, however, could return to the court in various forms. There is currently litigation in lower courts, for example, over whether such claims can be brought against companies that insure homeowners.

"The banking and insurance industries have set their sights on attacking the disparate impact standard," said Kristen Clarke, president of the Lawyers' Committee for Civil Rights Under Law. She urged senators to grill Trump's nominee on the issue during the Senate confirmation process expected later this summer.

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Some behaviors vary by rendering option . strict: true allows only those inputs that work in LaTeX. strict: false or strict: "warn" (default) accept a looser syntax.

For a list of things that are not (yet) in KaTeX, there is a wiki page .

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The letters listed above will render in any KaTeX rendering mode.

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KaTeX 0.10.0+ will insert automatic line breaks in inline math after relations or binary operators such as = or + . These can be suppressed by placing math inside a pair of braces, as in {F=ma} .

Hard line breaks are \\ and \newline .

In display math, KaTeX does not insert automatic line breaks. It ignores display math hard line breaks when rendering option strict: true .

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Objective Bayesian Two Sample Hypothesis Testing or Online Controlled Experiments Alex Deng

As A/B testing gains wider adoption in the industry, more people begin to realize the limitations of the traditional frequentist null hypothesis statistical testing (NHST). The large number of search results for the query ``Bayesian A/B testing'' shows just how much the interest in the Bayesian perspective is growing. In recent years there are also voices arguing that Bayesian A/B testing should replace frequentist NHST and is strictly superior in all aspects. Our goal here is to clarify the myth by looking at both advantages and issues of Bayesian methods. In particular, we propose an objective Bayesian A/B testing framework for which we hope to bring the best from Bayesian and frequentist methods together. Unlike traditional methods, this method requires the existence of historical A/B test data to objectively learn a prior. We have successfully applied this method to Bing, using thousands of experiments to establish the priors.

Diluted Treatment Effect Estimation for Trigger Analysis in Online Controlled Experiments Alex Deng and Victor Hu

Online controlled experiments, also called A/B testing, is playing a central role in many data-driven web-facing companies. It is well known and intuitively obvious to many practitioners that when testing a feature with low coverage, analyzing all data collected without zooming into the part that could be affected by the treatment often leads to under-powered hypothesis testing. A common practice is to use triggered analysis. To estimate the overall treatment effect, certain dilution formula is then applied to translate the estimated effect in triggered analysis back to the original all up population. In this paper, we discuss two different types of trigger analyses. We derive correct dilution formulas and show for a set of widely used metrics, namely ratio metrics, correctly deriving and applying those dilution formulas are not trivial. We observe many practitioners in this industry are often applying approximate formulas or even wrong formulas when doing effect dilution calculation. To deal with that, instead of estimating trigger treatment effect followed by effect translation using dilution formula, we aim at combining these two steps into one streamlined analysis, producing more accurate estimation of overall treatment effect together with even higher statistical power than a triggered analysis. The approach we propose in this paper is intuitive, easy to apply and general enough for all types of triggered analyses and all types of metrics.

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Ron Kohavi, Alex Deng, Roger Longbotham, and Ya Xu

Web site owners, from small web sites to the largest properties that include Amazon, Facebook, Google, LinkedIn, Microsoft, and Yahoo, attempt to improve their web sites, optimizing for criteria ranging from repeat usage, time on site, to revenue. Having been involved in running thousands of controlled experiments at Amazon, Booking.com, LinkedIn, and multiple Microsoft properties, we share seven rules of thumb for experimenters, which we have generalized from these experiments and their results. These are principles that we believe have broad applicability in web optimization and analytics outside of controlled experiments, yet they are not provably correct, and in some cases exceptions are known.

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One of the most surprising aspects of the current tax debate is that Republican leaders in Congress, who for years have railed against tax increases, are now advancing a bill that would raise taxes on millions of families even while it reduces taxes overall, especially for corporations, and increases the budget deficit. Most taxpayers would initially pay less than they do today under both the House and Senate bills, but some would pay more right away and many more would face tax increases throughout the decade.

Tax increases under the proposals would be most common for those who currently pay higher state and local taxes, which can be deducted on the federal tax return today. The tax legislation making its way through Congress would eliminate most of this deduction by limiting it to a maximum $10,000 deduction for property taxes. State and local income taxes would not be deductible at all.

Republicans in Congress reportedly are considering two versions of a change they claim would “improve” the current bills by making them more generous to residents of higher-taxed states. As illustrated by the numbers below, the reality is that these proposals would make little difference on those states and taxpayers hit hardest.

One “compromise” being discussed would allow taxpayers to deduct both state and local income taxes and property taxes, but the combined amount deducted would be capped at $10,000.

Another possible version of the compromise would be even less generous and have even less impact on the households hurt by the Senate tax bill. Under this scenario, taxpayers would be forced to choose deducting either state and local income taxes or deducting property taxes, and they could only deduct up to $10,000 of whichever they choose. This is less generous than the previous compromise described because some taxpayers might have both income taxes and property taxes to deduct but have less than $10,000 of either. For example, if a taxpayer paid $6,000 in state income taxes and $4,000 in property taxes, under this less generous compromise, the taxpayer would deduct the larger of the two (the $6,000 in state income taxes) but would not be able to deduct the combined amount of $10,000.

While these changes would add significant costs to the bill — almost $9 billion or $6 billion in the first year — it would have no significant impact for taxpayers hurt by the current bill and nearly no impact for low- and middle-income households.

Nationally, the total tax cuts and average tax cut in 2019 would be just 3 percent or 2 percent greater than under the Senate bill as passed. For the bottom three-fifths of taxpayers (the lowest-income 60 percent of taxpayers) this “compromise” would increase the average tax cut by just $2 to $5. Whereas the Senate bill as passed would raise taxes on about 9 percent of taxpayers, this compromise would drop that share to 7 percent or 8 percent of taxpayers.

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The University of Tennessee

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