# ➔ Phenomenal World

## ARTIFICIAL INFERENCE

### Causal reasoning and machine learning

In a recent paper titled "The Seven Pillars of Causal Reasoning with Reflections on Machine Learning", JUDEA PEARL, professor of computer science at UCLA and author of Causality, writes:

“Current machine learning systems operate, almost exclusively, in a statistical or model-free mode, which entails severe theoretical limits on their power and performance. Such systems cannot reason about interventions and retrospection and, therefore, cannot serve as the basis for strong AI. To achieve human level intelligence, learning machines need the guidance of a model of reality, similar to the ones used in causal inference tasks. To demonstrate the essential role of such models, I will present a summary of seven tasks which are beyond reach of current machine learning systems and which have been accomplished using the tools of causal modeling."

The tasks include work on counterfactuals, and new approaches to handling incomplete data. Link to the paper. A vivid expression of the issue: "Unlike the rules of geometry, mechanics, optics or probabilities, the rules of cause and effect have been denied the benefits of mathematical analysis. To appreciate the extent of this denial, readers would be stunned to know that only a few decades ago scientists were unable to write down a mathematical equation for the obvious fact that 'mud does not cause rain.' Even today, only the top echelon of the scientific community can write such an equation and formally distinguish 'mud causes rain' from 'rain causes mud.'”

Pearl also has a new book out, co-authored by DANA MCKENZIE, in which he argues for the importance of determining cause and effect in the machine learning context. From an interview in Quanta magazine about his work and the new book:

"As much as I look into what’s being done with deep learning, I see they’re all stuck there on the level of associations. Curve fitting. That sounds like sacrilege, to say that all the impressive achievements of deep learning amount to just fitting a curve to data. If we want machines to reason about interventions ('What if we ban cigarettes?') and introspection ('What if I had finished high school?'), we must invoke causal models. Associations are not enough—and this is a mathematical fact, not opinion.

We have to equip machines with a model of the environment. If a machine does not have a model of reality, you cannot expect the machine to behave intelligently in that reality. The first step, one that will take place in maybe 10 years, is that conceptual models of reality will be programmed by humans."

## SHOCK-LEVEL-ZERO

### Jobs guarantees vs. basic income

In a characteristically lengthy and thorough post, SCOTT ALEXANDER of SLATE STAR CODEX argues for a basic income over a jobs guarantee, in dialogue with a post by SIMON SARRIS.

Here's how Alexander addresses the claim that “studies of UBI haven’t been very good, so we can’t know if it works”:

“If we can’t 100% believe the results of small studies – and I agree that we can’t – our two options are to give up and never do anything that hasn’t already been done, or to occasionally take the leap towards larger studies. I think basic income is promising enough that we need to pursue the second. Sarris has already suggested he won’t trust anything that’s less than permanent and widespread, so let’s do an experiment that’s permanent and widespread.”

Link to the full piece on Slate Star.

For another angle on the same question, MARTIN RAVALLION recently published a paper at the CENTER FOR GLOBAL DEVELOPMENT looking at employment guarantees and income guarantees primarily in India:

“The paper has pointed to evidence for India suggesting that the country’s Employment Guarantee Schemes have been less cost effective in reducing current poverty through the earnings gains to workers than one would expect from even untargeted transfers, as in a UBI. This calculation could switch in favor of workfare schemes if they can produce assets of value (directly or indirectly) to poor people, though the evidence is mixed on this aspect of the schemes so far in India.”

Ravallion takes a nuanced view of arguments for the right to work and the right to income, as well as the constraints of implementation, and concludes, "The key point is that, in some settings, less effort at fine targeting may well prove to be more cost-effective in assuring economic freedom from material deprivation." Full study available here. ht Sidhya

## Lay of the Land

### A new paper on the labor effects of cash transfers

SARAH BAIRD, DAVID MCKENZIE, and BERK OZLER of the WORLD BANK review a variety of cash transfer studies, both governmental and non-governmental, in low- and middle-income countries. Cash transfers aren’t shown to have the negative effects on work that some fear:

"The basic economic model of labor supply has a very clear prediction of what should be expected when an adult receives an unexpected cash windfall: they should work less and earn less. This intuition underlies concerns that many types of cash transfers, ranging from government benefits to migrant remittances, will undermine work ethics and make recipients lazy.

Overall, cash transfers that are made without an explicit employment focus (such as conditional and unconditional cash transfers and remittances) tend to result in little to no change in adult labor. The main exceptions are transfers to the elderly and some refugees, who reduce work. In contrast, transfers made for job search assistance or business start-up tend to increase adult labor supply and earnings, with the likely main channels being the alleviation of liquidity and risk constraints."

Link to the working paper. Table 2—which covers the channels through which cash impacts labor, is especially worth a read—as many studies on cash transfers don’t go into this level of detail.

• A study on a large-scale unconditional cash transfer in Iran: "With the exception of youth, who have weak ties to the labor market, we find no evidence that cash transfers reduced labor supply, while service sector workers appear to have increased their hours of work, perhaps because some used transfers to expand their business." Link.
• Continuing the analysis of Hauschofer and Schapiro’s controversial results from a cash study transfer in Kenya, Josh Rosenberg at GiveDirectly has, at the end of his overview, some thoughtful questions for continuing research: "Is our cost-effectiveness model using a reasonable framework for estimating recipients’ standard of living over time?… GiveDirectly provides large, one-time transfers whereas many government cash transfers provide smaller ongoing support to poor families. How should we apply new literature on other kinds of cash programs to our estimates of the effects of GiveDirectly?" Link.

## POSTAL OPTION

### Renewed interest in an old model

Last week we linked to the widely publicized news that SENATOR KIRSTEN GILLIBRAND would be pushing legislation to reintroduce government-run commercial banking through the United States Postal Service.

In a 2014 article for the HARVARD LAW REVIEW, law professor and postal banking advocate MEHRSA BARADARAN describes the context that makes postal banking an appealing solution:

“Credit unions, S&Ls, and Morris Banks were alternatives to mainstream banks, but they were all supported and subsidized by the federal government through targeted regulation and deposit insurance protection.

Banking forms homogenized in the 1970s and 1980s, leaving little room for variation in institutional or regulatory design. Eventually, each of these institutions drifted from their initial mission of serving the poor and began to look more like commercial banks, even competing with them for ever-shrinking profit margins.

The result now is essentially two forms of banks: regulated mainstream banks that seek maximum profit for their shareholders by serving the needs of the wealthy and middle class, and unregulated fringe banks that seek maximum profits for their shareholders by serving the banking and credit needs of the poor. What is missing from the American banking landscape for the first time in almost a century is a government-sponsored bank whose main purpose is to meet the needs of the poor."

## ONTARIO FOR ALL

### Canada calculates expanding Ontario's guaranteed income to the entire nation

Canada’s Parliamentary Budget Office looks at the cost of expanding the Ontario pilot nationwide. Full report here. ht Lauren

ANDREW COYNE of the NATIONAL POST summarizes the findings (all figures are in Canadian dollars):

“The results, speculative as they are, are intriguing. The PBO puts the cost of a nationwide rollout of the Ontario program, guaranteeing every adult of working age a minimum of 16,989 CAD annually (24,027 CAD for couples), less 50 per cent of earned income—there’d also be a supplement of up to 6,000 CAD for those with a disability—at 76.0 billion CAD.
“Even that number, eye-watering as it is (the entire federal budget, for reference, is 312 billion CAD), is a long way from the 500 billion CAD estimates bandied about in some quarters.

## URBAN WEALTH FUNDS

### Social wealth funds on the municipal level

Matt Bruenig, Roger Farmer and Miles Kimball, and Sam Altman have all pushed for versions of a US sovereign wealth fund for social good. Their work focuses on funds at the national level. But another version of the idea comes from Dag Detter and Stefan Fölster, whose 2017 book advocates for “urban wealth funds,” funded via better management of government land and other nonfinancial assets. A few such funds have already had success.

Using Boston as an example of a city that could profit from an urban wealth fund, Detter writes for the World Economic Forum in February:

“…Like many other cities, Boston does not assess the market value of its economic assets. Unlocking the public value of poorly utilized real estate or monetizing its transportation and utility assets – smarter asset management, in other words – would yield a return that would enable it to more than double its infrastructure investments. Through smarter asset management, Boston could improve its public transport system and other services without needing to opt for privatization, raise taxes or cut spending elsewhere.

“What’s the catch? Actually, there isn’t one.”

Link to the full post. A 2017 Brookings report showed how Copenhagen successfully implemented urban wealth fund policy:

“This paper explores how the Copenhagen model can revitalize cities and finance large-scale infrastructure by increasing the commercial yield of publicly owned land and buildings without raising taxes. The approach deploys an innovative institutional vehicle—a publicly owned, privately run corporation—to achieve the high-level management and value appreciation of assets more commonly found in the private sector while retaining development profits for public use.”

• Another successful version of urban value capture: Hong Kong’s metro (the MTR). “Hong Kong is one of the world’s densest cities, and businesses depend on the metro to ferry customers from one side of the territory to another. As a result, the MTR strikes a bargain with shop owners: In exchange for transporting customers, the transit agency receives a cut of the mall’s profit, signs a co-ownership agreement, or accepts a percentage of property development fees. In many cases, the MTR owns the entire mall itself.” Link.
• Detter and Fölster’s previous book envisions better management of government assets on the national level.

## DISTINCT FUSION

### Tracking the convergence of terms across disciplines

In a new paper, CHRISTIAN VINCENOT looks at the process by which two synonymous concepts developed independently in separate disciplines, and how they were brought together.

“I analyzed research citations between the two communities devoted to ACS research, namely agent-based (ABM) and individual-based modelling (IBM). Both terms refer to the same approach, yet the former is preferred in engineering and social sciences, while the latter prevails in natural sciences. This situation provided a unique case study for grasping how a new concept evolves distinctly across scientific domains and how to foster convergence into a universal scientific approach. The present analysis based on novel hetero-citation metrics revealed the historical development of ABM and IBM, confirmed their past disjointedness, and detected their progressive merger. The separation between these synonymous disciplines had silently opposed the free flow of knowledge among ACS practitioners and thereby hindered the transfer of methodological advances and the emergence of general systems theories. A surprisingly small number of key publications sparked the ongoing fusion between ABM and IBM research.”

Link to a summary and context. Link to the abstract. ht Margarita

• Elsewhere in metaresearch, a new paper from James Evans’s Knowledge Lab examines influence by other means than citations: “Using a computational method known as topic modeling—invented by co-author David Blei of Columbia University—the model tracks ‘discursive influence,’ or recurring words and phrases through historical texts that measure how scholars actually talk about a field, instead of just their attributions. To determine a given paper’s influence, the researchers could statistically remove it from history and see how scientific discourse would have unfolded without its contribution.” Link to a summary. Link to the full paper.