.. _replication-lamba-tigers: Tiger reserves and avoided deforestation (Lamba et al. 2023) ============================================================ :Estimator: :doc:`../vanillasc` — :class:`mlsynth.VanillaSC` :Source: Lamba, A., Teo, H. C., Sreekar, R., Zeng, Y., Carrasco, L. R., & Koh, L. P. (2023), *"Climate co-benefits of tiger conservation,"* Nature Ecology & Evolution 7:1104–1113, `doi:10.1038/s41559-023-02069-x `_. :Replication type: cross-validation against the R package ``tidysynth``. :Status: Seam verified exactly; the estimates deliberately not matched, for reasons this page sets out. :Case: `benchmarks/cases/lamba_tigers.py `_ What the paper does ------------------- India designates certain protected areas as tiger reserves, which brings extra funding, patrolling and monitoring. The paper asks whether that protection also slowed deforestation, and converts the answer into avoided carbon emissions. The design is one synthetic control per reserve. For a reserve designated in, say, 2013, the outcome is cumulative forest loss in hectares from 2001 to 2020; the donor pool is the protected areas in the same tiger-conservation zone that were never designated; and the synthetic control is fit on the years before designation using thirteen predictors — the outcome's own pre-period mean, rainfall, population density, baseline forest area, the reserve's age, elevation, slope, aspect, area, travel time to the nearest city, purchasing power, above-ground biomass and road length. The gap in 2020 between the reserve's observed forest loss and its synthetic counterpart is the avoided loss. Significance comes from the usual placebo exercise: refit treating each donor as if it had been designated, and rank the reserve's ratio of post-treatment to pre-treatment prediction error among them. Forty-five reserves qualify, adopting across nine different years, with donor pools of twenty to forty-six that differ reserve by reserve. That shape is why the case exists: no other synthetic-control benchmark in mlsynth has staggered adoption with a donor pool that changes per treated unit. The paper reproduces — on a superseded package ---------------------------------------------- The replication package is unusually complete: 184 KB holding the data, the estimation script, the Google Earth Engine code that produced the covariates, and a metadata file naming every source. Running it reproduces Table 1 essentially exactly: .. list-table:: Avoided forest loss (ha), published against the authors' script :header-rows: 1 :widths: 44 18 18 12 * - Reserve - Published - ``tidysynth`` 0.1.0 - Difference * - Nawegaon–Nagzira - 2,645 - 2,643.4 - −1.6 * - Similipal–Hadagarh - 1,570 - 1,569.9 - −0.1 * - Udanti - 1,611 - 1,608.0 - −3.0 * - Valmiki - 315 - 317.5 - +2.5 * - Nagarjuna Sagar–Srisailam - 167 - 187.1 - +20.1 * - Palamau - 143 - 125.4 - −17.6 * - Satkosia Gorge - 39 - 35.7 - −3.3 * - Bandipur - 25 - 25.4 - +0.4 * - Biligiri Rangaswamy Temple - 22 - 21.6 - −0.4 * - Sariska - 14 - 14.2 - +0.2 * - Satpura - 9 - 8.7 - −0.3 * - Total - 6,560 - 6,556.7 - −3.3 That is a clean result, and rarer than it should be. The catch is the version. The authors' script calls ``grab_signficance`` — a misspelling ``tidysynth`` corrected on 2021-11-30 — so their analysis predates a 2022-08-31 commit whose own message says time-ordering problems "resulted in downstream distortions of the package output". The numbers above come from a build older than that fix. Why mlsynth is not checked against those numbers ------------------------------------------------ Because reproducing them means reproducing a defect the package's own maintainer has since corrected. The reference bundle therefore runs ``tidysynth`` 0.2.0, the current release, and the table above is recorded here as history rather than pinned anywhere. Running the current release surfaces two further problems, both documented in the reference script because they bound what this replication can claim. It ignores ``quadopt``. The authors pass ``quadopt = "LowRankQP"``, still documented in the package's help. In 0.2.0 ``R/synth.R:174`` calls ``kernlab::ipop`` unconditionally inside the predictor-weight search, and the final solve at line 303 sits inside an ``if (quadopt == "ipop")`` with no ``else`` branch, so the ``LowRankQP`` path assigns no weights at all. Version 0.1.0 had eight ``LowRankQP`` call sites; 0.2.0 has only the argument validation, after a 2023-05-20 commit dropped the dependency. The reference passes ``"ipop"`` explicitly, which is what the package does either way. It cannot fit four of the fifteen reserves. ``ipop`` fails outright on Valmiki, Biligiri Rangaswamy Temple, Sariska and Anamalai with a computationally singular system, on panels the older build fitted without complaint. Those failures are captured in ``failures.csv`` and counted in the case rather than dropped quietly, because a reference that hid them would overstate how much of the paper the current package can still reproduce. The estimates differ, and the reason is identification ------------------------------------------------------ Even setting versions aside, mlsynth and ``tidysynth`` do not agree on the effects — and the interesting part is *where* they agree. The predictor matrix agrees exactly. Both implementations reduce the panel to thirteen rows by one column per unit, and mlsynth reproduces ``tidysynth``'s matrix to nine significant figures across every donor of every reserve. That is the seam, it is a deterministic function of the data, and the benchmark gates it tightly. Whatever else differs, the panel does not. The weights do not agree. Abadie's method chooses the predictor weights :math:`V` by an outer search that minimises pre-treatment prediction error, and that search is not identified when predictors are numerous relative to pre-periods — thirteen against as few as six here. On the headline reserve ``tidysynth`` returns a :math:`V` placing 1.2 percent of the weight on the outcome's own history and 58 percent on elevation, and lands on a pre-treatment fit 3.6 times worse than mlsynth's. Across the eleven reserves the current package can fit, mlsynth reaches the lower pre-treatment error on ten; the exception is Similipal–Hadagarh, where ``tidysynth`` fits slightly better (334.5 against 364.4). Holding mlsynth to agreement would therefore hold it to the worse solution on most of the panel, so the benchmark records the comparison and gates mlsynth's own values instead. The count of ten is itself pinned, so a change in either direction fails and has to be looked at. Two further properties of mlsynth's solutions on this panel are pinned because they are not what one would guess. The donor weights are interior — all forty-four donors carry weight on the headline reserve, the smallest about 0.019, where a simplex least-squares fit usually zeroes most of them. And the :math:`V` search is mildly seed-sensitive on short pre-periods: Udanti moves about 21 ha (1.4 percent) across seeds, while Nawegaon–Nagzira and Similipal–Hadagarh are stable to about 1e-5. The benchmark's tolerances are set from that measured spread rather than guessed. A note on the estimand, since it is easy to get wrong. The paper's avoided loss is the terminal-year gap, synthetic minus observed in 2020. That is not :attr:`~mlsynth.config_models.EffectsResults.att`, which averages observed minus synthetic over every post-treatment year — on the headline reserve the two are +2825 and −1491. An earlier draft of this case substituted one for the other and produced entirely plausible numbers; the reference comparison is what caught it, and a test now pins the distinction. How much of the disagreement is real ambiguity rather than one side being wrong can be measured. Among all synthetic controls fitting the pre-period within two percent of the best attainable, the avoided loss on the headline reserve can lie anywhere in [2768, 2922] ha; widen to ten percent and the range is [2594, 3026]. mlsynth's 2,825 ha sits inside the tight band and the published 2,645 ha inside the wider one, so both are defensible readings of the same data. The current ``tidysynth``'s 1,999 ha falls outside even a fifty-percent band, which is what marks it as a poor fit rather than a rival answer. That diagnostic is not yet a library feature; it is tracked in `issue #310 `_. What to take from this ---------------------- The paper's conclusion is not in question. Every implementation tried here agrees that the headline reserves averted substantial forest loss, that Nawegaon–Nagzira averted the most, and that a minority of reserves lost more forest than their counterfactuals. What is fragile is the third digit, and anyone quoting a specific hectare figure should know it moves by hundreds of hectares depending on which build of which package produced it. For mlsynth's purposes the case pins two things worth pinning: that the panel construction is exactly right, and that the estimator handles a staggered, per-reserve, zone-restricted design without incident.